title
Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy

description
January 10, 2023 Introduction to Transformers Andrej Karpathy: https://karpathy.ai/ Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs), Speech or even Biology. Among other things, transformers have enabled the creation of powerful language models like GPT-3 and were instrumental in DeepMind's recent AlphaFold2, that tackles protein folding. In this speaker series, we examine the details of how transformers work, and dive deep into the different kinds of transformers and how they're applied in different fields. We do this by inviting people at the forefront of transformers research across different domains for guest lectures. More about the course can be found here: https://web.stanford.edu/class/cs25/ View the entire CS25 Transformers United playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM 0:00 Introduction 0:47 Introducing the Course 3:19 Basics of Transformers 3:35 The Attention Timeline 5:01 Prehistoric Era 6:10 Where we were in 2021 7:30 The Future 10:15 Transformers - Andrej Karpathy 10:39 Historical context 1:00:30 Thank you - Go forth and transform

detail
{'title': 'Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy', 'heatmap': [{'end': 645.791, 'start': 601.049, 'weight': 0.814}, {'end': 1593.851, 'start': 1374.769, 'weight': 0.833}, {'end': 1980.064, 'start': 1932.039, 'weight': 0.809}, {'end': 3269.355, 'start': 3222.362, 'weight': 0.79}], 'summary': 'Stanford cs25: v2 introduces the impact of deep learning models like transformers in fields such as natural language processing, computer vision, reinforcement learning, biology, and robotics. it covers the evolution of transformers from 2017 to the present, transformer architecture, attention mechanism, and its applications in ml, nlp, and cv, with reference to over 200 papers.', 'chapters': [{'end': 140.39, 'segs': [{'end': 41.414, 'src': 'embed', 'start': 6.041, 'weight': 0, 'content': [{'end': 6.441, 'text': 'Hi, everyone.', 'start': 6.041, 'duration': 0.4}, {'end': 9.083, 'text': 'Welcome to CS25 Transformers United V2.', 'start': 7.002, 'duration': 2.081}, {'end': 13.587, 'text': 'This was a course that was held at Stanford in the winter of 2023.', 'start': 9.884, 'duration': 3.703}, {'end': 17.109, 'text': 'This course is not about robots that can transform into cars, as this picture might suggest.', 'start': 13.587, 'duration': 3.522}, {'end': 23.234, 'text': "Rather, it's about deep learning models that have taken the world by the storm and have revolutionized the field of AI and others.", 'start': 17.63, 'duration': 5.604}, {'end': 25.456, 'text': 'Starting from natural language processing.', 'start': 24.075, 'duration': 1.381}, {'end': 30.98, 'text': 'transformers have been applied all over, from computer vision reinforcement, learning, biology, robotics, et cetera.', 'start': 25.456, 'duration': 5.524}, {'end': 34.269, 'text': 'We have an exciting set of videos lined up for you,', 'start': 32.128, 'duration': 2.141}, {'end': 41.414, 'text': "with some truly fascinating speakers give talks presenting how they're applying transformers to the research in different fields and areas.", 'start': 34.269, 'duration': 7.145}], 'summary': 'Cs25 transformers united v2 at stanford in winter 2023 explored diverse applications of transformers in ai, including natural language processing, computer vision, reinforcement learning, biology, and robotics.', 'duration': 35.373, 'max_score': 6.041, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E6041.jpg'}, {'end': 79.312, 'src': 'embed', 'start': 47.878, 'weight': 2, 'content': [{'end': 49.839, 'text': "So without any further ado, let's get started.", 'start': 47.878, 'duration': 1.961}, {'end': 57.263, 'text': "This is a purely introductory lecture, and we'll go into the building blocks of transformers.", 'start': 52.1, 'duration': 5.163}, {'end': 61.466, 'text': "So first, let's start with introducing the instructors.", 'start': 58.944, 'duration': 2.522}, {'end': 69.965, 'text': "So for me, I'm currently on a temporary defer from the PhD program and I'm leading care at a robotic startup, collaborative robotics,", 'start': 63.52, 'duration': 6.445}, {'end': 74.468, 'text': 'working on some general purpose robots, somewhat like Testabot.', 'start': 69.965, 'duration': 4.503}, {'end': 79.312, 'text': "And yeah, I'm very passionate about robotics and building efficient learning algorithms.", 'start': 75.169, 'duration': 4.143}], 'summary': 'Introductory lecture on transformer building blocks, presented by a phd candidate leading care at a robotic startup, focusing on general purpose robots and efficient learning algorithms.', 'duration': 31.434, 'max_score': 47.878, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E47878.jpg'}, {'end': 121.993, 'src': 'embed', 'start': 93.905, 'weight': 1, 'content': [{'end': 96.486, 'text': "So I'm Steven, currently first year CSB student here.", 'start': 93.905, 'duration': 2.581}, {'end': 100.187, 'text': "I previously did my master's at CMU and undergrad at Waterloo.", 'start': 97.066, 'duration': 3.121}, {'end': 103.908, 'text': "I'm mainly into NLP research, anything involving language and text.", 'start': 100.847, 'duration': 3.061}, {'end': 110.429, 'text': "But more recently, I've been getting more into computer vision as well as and just some stuff I do for fun.", 'start': 104.568, 'duration': 5.861}, {'end': 112.59, 'text': 'A lot of music stuff, mainly piano.', 'start': 110.449, 'duration': 2.141}, {'end': 116.931, 'text': 'Some self promo, but I post a lot on my Insta, YouTube, and TikTok.', 'start': 113.07, 'duration': 3.861}, {'end': 118.752, 'text': 'So if you guys wanna check it out.', 'start': 117.071, 'duration': 1.681}, {'end': 121.993, 'text': 'My friends and I are also starting a Stanford piano club.', 'start': 118.832, 'duration': 3.161}], 'summary': 'Steven is a first-year csb student interested in nlp research and computer vision. he is also active in music and social media, and is starting a stanford piano club with friends.', 'duration': 28.088, 'max_score': 93.905, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E93905.jpg'}], 'start': 6.041, 'title': 'Cs25 transformers united v2', 'summary': 'Introduces the cs25 transformers united v2 course at stanford in 2023, exploring the impact of deep learning models like transformers in fields such as natural language processing, computer vision, reinforcement learning, biology, and robotics.', 'chapters': [{'end': 140.39, 'start': 6.041, 'title': 'Cs25 transformers united v2', 'summary': 'Introduces the cs25 transformers united v2 course held at stanford in 2023, covering the revolutionary impact of deep learning models like transformers in various fields such as natural language processing, computer vision, reinforcement learning, biology, and robotics.', 'duration': 134.349, 'highlights': ['The course covers the revolutionary impact of deep learning models like transformers in various fields such as natural language processing, computer vision, reinforcement learning, biology, and robotics.', 'The instructors, including a PhD student and a first-year CSB student, share their backgrounds and research interests in robotics, autonomous driving, personal learning, computer vision, NLP, and music.', 'The chapter includes introductions from the instructors, including their educational backgrounds and personal interests such as music, martial arts, bodybuilding, and gaming.']}], 'duration': 134.349, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E6041.jpg', 'highlights': ['The course covers the revolutionary impact of deep learning models like transformers in various fields such as natural language processing, computer vision, reinforcement learning, biology, and robotics.', 'The instructors, including a PhD student and a first-year CSB student, share their backgrounds and research interests in robotics, autonomous driving, personal learning, computer vision, NLP, and music.', 'The chapter includes introductions from the instructors, including their educational backgrounds and personal interests such as music, martial arts, bodybuilding, and gaming.']}, {'end': 649.953, 'segs': [{'end': 211.808, 'src': 'embed', 'start': 181.475, 'weight': 1, 'content': [{'end': 183.956, 'text': 'How are they being applied?', 'start': 181.475, 'duration': 2.481}, {'end': 189.857, 'text': 'And nowadays we are pretty much everywhere in AI machine learning.', 'start': 184.476, 'duration': 5.381}, {'end': 197.92, 'text': 'And what are some new interventions or research in these topics? Cool.', 'start': 190.378, 'duration': 7.542}, {'end': 199.981, 'text': 'So this class is just an introductory.', 'start': 198.4, 'duration': 1.581}, {'end': 206.103, 'text': "So we're just talking about the basics of transformers, introducing them, talking about the self-attention mechanism on which they're founded.", 'start': 200.021, 'duration': 6.082}, {'end': 211.808, 'text': "And we'll do a deep dive more on models like BERT, GPT, stuff like that.", 'start': 206.904, 'duration': 4.904}], 'summary': 'Ai machine learning applied everywhere, introducing basics of transformers and deep diving into models like bert and gpt.', 'duration': 30.333, 'max_score': 181.475, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E181475.jpg'}, {'end': 279.769, 'src': 'embed', 'start': 254.072, 'weight': 2, 'content': [{'end': 264.319, 'text': 'uh, for the last, 2018, after 2018 to 2020, we saw this explosion of transformers into other few, like vision, um a bunch of other stuff and, uh,', 'start': 254.072, 'duration': 10.247}, {'end': 270.784, 'text': 'like biology, alcohol And last year, 2021, was the start of the generative era, where we got a lot of generative modeling.', 'start': 264.319, 'duration': 6.465}, {'end': 276.367, 'text': 'Started models like Codex, GPT, DALI, stable diffusion.', 'start': 271.544, 'duration': 4.823}, {'end': 279.769, 'text': 'So a lot of things happening in generative modeling.', 'start': 276.407, 'duration': 3.362}], 'summary': 'From 2018-2020, saw transformers expanding into various fields. in 2021, generative era began with models like codex, gpt, and dali.', 'duration': 25.697, 'max_score': 254.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E254072.jpg'}, {'end': 395.987, 'src': 'embed', 'start': 365.43, 'weight': 0, 'content': [{'end': 368.471, 'text': 'And this works better than existing mechanisms.', 'start': 365.43, 'duration': 3.041}, {'end': 376.319, 'text': 'OK So where we were in 2021, we were on the verge of takeoff.', 'start': 370.352, 'duration': 5.967}, {'end': 379.96, 'text': 'We were starting to realize the potential of transformers in different fields.', 'start': 377.079, 'duration': 2.881}, {'end': 386.883, 'text': 'We solved a lot of long sequence problems like protein folding, alpha fold, offline RL.', 'start': 380.881, 'duration': 6.002}, {'end': 391.806, 'text': 'We started to see zero-shot generalization.', 'start': 388.864, 'duration': 2.942}, {'end': 395.987, 'text': 'We saw multimodal tasks and applications like generating images from language.', 'start': 391.906, 'duration': 4.081}], 'summary': 'In 2021, there was progress in transformers, solving long sequence problems like protein folding, alpha fold, offline rl, and zero-shot generalization, as well as multimodal tasks and applications.', 'duration': 30.557, 'max_score': 365.43, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E365430.jpg'}, {'end': 497.359, 'src': 'embed', 'start': 467.943, 'weight': 3, 'content': [{'end': 469.925, 'text': 'one big example is video understanding and generation.', 'start': 467.943, 'duration': 1.982}, {'end': 475.709, 'text': "that is something that everyone is interested in and i'm hoping we'll see a lot of models in this area this year.", 'start': 469.925, 'duration': 5.784}, {'end': 479.872, 'text': 'also finance business.', 'start': 475.709, 'duration': 4.163}, {'end': 482.794, 'text': "i'll be very excited to see gbt author novel.", 'start': 479.872, 'duration': 2.922}, {'end': 490.279, 'text': 'but we need to solve very long sequence modeling and most transformers models are still limited to like 4 000 tokens or something like that.', 'start': 482.794, 'duration': 7.485}, {'end': 494.458, 'text': 'so we need to do a make them generalize much more better.', 'start': 490.279, 'duration': 4.179}, {'end': 497.359, 'text': 'on long sequences, uh, we are also.', 'start': 494.458, 'duration': 2.901}], 'summary': 'Interest in video understanding, generation, finance, and novel authoring; need to improve long sequence modeling for transformers.', 'duration': 29.416, 'max_score': 467.943, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E467943.jpg'}, {'end': 647.272, 'src': 'heatmap', 'start': 585.909, 'weight': 4, 'content': [{'end': 591.617, 'text': 'A lot of these models can be stochastic, and we want to be able to control what sort of outputs we get from them.', 'start': 585.909, 'duration': 5.708}, {'end': 596.885, 'text': 'And you might have experienced with chat, GPT features, refresh get like different output each time,', 'start': 592.478, 'duration': 4.407}, {'end': 601.049, 'text': 'but you might want to have mechanisms that controls what sort of things you get.', 'start': 596.885, 'duration': 4.164}, {'end': 606.354, 'text': 'uh. and finally, we want to align our state of art language models with how the human brain works.', 'start': 601.049, 'duration': 5.305}, {'end': 612.159, 'text': 'and, uh, we are seeing the search, but we still need more research on seeing how they can be more important.', 'start': 606.354, 'duration': 5.805}, {'end': 615.663, 'text': 'okay, thank you, great bye.', 'start': 612.159, 'duration': 3.504}, {'end': 617.731, 'text': "Yes, I'm excited to be here.", 'start': 616.81, 'duration': 0.921}, {'end': 619.392, 'text': 'I live very nearby.', 'start': 618.291, 'duration': 1.101}, {'end': 622.655, 'text': "So I got the invites to come to class and I was like, okay, I'll just walk over.", 'start': 619.492, 'duration': 3.163}, {'end': 625.498, 'text': 'But then I spent like 10 hours in those slides.', 'start': 623.816, 'duration': 1.682}, {'end': 626.979, 'text': "So it wasn't as simple.", 'start': 625.558, 'duration': 1.421}, {'end': 630.122, 'text': 'So yeah, I want to talk about transformers.', 'start': 628.46, 'duration': 1.662}, {'end': 632.504, 'text': "I'm going to skip the first two over there.", 'start': 630.782, 'duration': 1.722}, {'end': 633.725, 'text': "We're not going to talk about those.", 'start': 632.784, 'duration': 0.941}, {'end': 635.146, 'text': "We'll talk about that one.", 'start': 633.765, 'duration': 1.381}, {'end': 636.828, 'text': "Just to simplify the lecture since we've got time.", 'start': 635.246, 'duration': 1.582}, {'end': 645.791, 'text': 'Okay So I wanted to provide a little bit of context of why does this Transformers class even exist? So a little bit of historical context.', 'start': 638.049, 'duration': 7.742}, {'end': 647.272, 'text': 'I feel like Bilbo over there.', 'start': 646.031, 'duration': 1.241}], 'summary': 'Discussion on controlling stochastic model outputs and aligning language models with human brain, need for more research.', 'duration': 61.363, 'max_score': 585.909, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E585909.jpg'}], 'start': 140.55, 'title': 'Transformers evolution', 'summary': 'Introduces an upcoming class on transformers, discussing the basics, applications, and advancements, highlighting the exponential growth of transformers from 2017 to the present. it also discusses the evolution of transformers in 2021, highlighting their successes in various fields and emphasizing the potential for future applications, while addressing challenges in long sequence modeling and controllability of models.', 'chapters': [{'end': 310.789, 'start': 140.55, 'title': 'Excitement for transformers class', 'summary': 'Introduces an upcoming class on transformers, discussing the basics, applications, and advancements, highlighting the exponential growth of transformers in various fields from 2017 to the present.', 'duration': 170.239, 'highlights': ["Transformers' growth from 2017 to present The chapter outlines the exponential growth of transformers in various fields from 2017 to the present, starting with the historic era of RNNs and LSTNs and the subsequent explosion of transformers into NLP, vision, biology, and generative modeling.", 'Introduction of transformers and self-attention mechanism The class aims to introduce the basics of transformers, including the self-attention mechanism on which they are founded, and delve deeper into models like BERT and GPT.']}, {'end': 649.953, 'start': 310.849, 'title': 'Evolution of transformers in 2021-2022', 'summary': 'Discusses the evolution of transformers in 2021, highlighting their successes in various fields, such as long sequence problems, zero-shot generalization, multimodal tasks, and unique applications in audio, art, music, and storytelling. it also emphasizes the potential for future applications in video understanding and generation, finance business, and domain-specific models. however, there are still challenges to address, including the need for improved long sequence modeling, enhanced controllability of models, and aligning state-of-the-art language models with human brain functionality.', 'duration': 339.104, 'highlights': ["Transformers' successes in 2021 include solving long sequence problems, zero-shot generalization, and multimodal tasks, and unique applications in audio, art, music, and storytelling. The chapter highlights the successes of transformers in 2021, such as solving long sequence problems like protein folding, alpha fold, offline RL, and experiencing zero-shot generalization. Additionally, it mentions the emergence of multimodal tasks and applications, such as generating images from language and unique applications in audio, art, music, and storytelling.", 'Potential future applications in video understanding and generation, finance business, and domain-specific models are emphasized. The discussion also emphasizes the potential for future applications in video understanding and generation, finance business, and domain-specific models, indicating the broadening scope of transformer technology beyond its current applications.', 'Challenges include the need for improved long sequence modeling, enhanced controllability of models, and aligning state-of-the-art language models with human brain functionality. The chapter addresses challenges such as the need for improved long sequence modeling, enhanced controllability of models, and aligning state-of-the-art language models with human brain functionality, highlighting the areas where further advancements and research are required.']}], 'duration': 509.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E140550.jpg', 'highlights': ["Transformers' successes in 2021 include solving long sequence problems, zero-shot generalization, and multimodal tasks, and unique applications in audio, art, music, and storytelling.", 'Introduction of transformers and self-attention mechanism The class aims to introduce the basics of transformers, including the self-attention mechanism on which they are founded, and delve deeper into models like BERT and GPT.', "Transformers' growth from 2017 to present The chapter outlines the exponential growth of transformers in various fields from 2017 to the present, starting with the historic era of RNNs and LSTNs and the subsequent explosion of transformers into NLP, vision, biology, and generative modeling.", 'Potential future applications in video understanding and generation, finance business, and domain-specific models are emphasized.', 'Challenges include the need for improved long sequence modeling, enhanced controllability of models, and aligning state-of-the-art language models with human brain functionality.']}, {'end': 1095.962, 'segs': [{'end': 690.332, 'src': 'embed', 'start': 649.973, 'weight': 3, 'content': [{'end': 651.874, 'text': "I don't know if you guys saw the drinks.", 'start': 649.973, 'duration': 1.901}, {'end': 657.778, 'text': 'And basically, I joined AI in roughly 2012 in full force, so maybe a decade ago.', 'start': 652.775, 'duration': 5.003}, {'end': 660.639, 'text': "And back then, you wouldn't even say that you joined AI, by the way.", 'start': 658.358, 'duration': 2.281}, {'end': 661.78, 'text': 'That was like a dirty word.', 'start': 660.679, 'duration': 1.101}, {'end': 663.941, 'text': "Now it's OK to talk about.", 'start': 662.54, 'duration': 1.401}, {'end': 665.562, 'text': 'But back then, it was not even deep learning.', 'start': 663.981, 'duration': 1.581}, {'end': 666.402, 'text': 'It was machine learning.', 'start': 665.582, 'duration': 0.82}, {'end': 667.343, 'text': "That was the term we'd use.", 'start': 666.482, 'duration': 0.861}, {'end': 668.506, 'text': 'if you were serious.', 'start': 667.925, 'duration': 0.581}, {'end': 671.251, 'text': 'But now AI is OK to use, I think.', 'start': 668.546, 'duration': 2.705}, {'end': 677.481, 'text': 'So, basically, do you even realize how lucky you are potentially entering this area in roughly 2023?', 'start': 672.052, 'duration': 5.429}, {'end': 685.068, 'text': 'So back then, in 2011 or so, when I was working specifically on computer vision, Your pipelines looked like this', 'start': 677.481, 'duration': 7.587}, {'end': 688.03, 'text': 'So you wanted to classify some images.', 'start': 686.148, 'duration': 1.882}, {'end': 690.332, 'text': 'You would go to a paper, and I think this is a representative.', 'start': 688.51, 'duration': 1.822}], 'summary': 'Joined ai in 2012, 2011 computer vision work, significant changes, now is a good time to enter ai in 2023.', 'duration': 40.359, 'max_score': 649.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E649973.jpg'}, {'end': 813.822, 'src': 'embed', 'start': 788.529, 'weight': 0, 'content': [{'end': 793.534, 'text': 'And so up till then, there was a lot of focus on algorithms, but this showed that actually neural nets scale very well.', 'start': 788.529, 'duration': 5.005}, {'end': 796.136, 'text': 'So you need to not worry about compute and data and you can scale it up.', 'start': 793.574, 'duration': 2.562}, {'end': 796.757, 'text': 'It works pretty well.', 'start': 796.156, 'duration': 0.601}, {'end': 801.108, 'text': 'And then that recipe actually did copy paste across many areas of AI.', 'start': 797.324, 'duration': 3.784}, {'end': 804.352, 'text': 'So we started to see neural networks pop up everywhere since 2012.', 'start': 801.669, 'duration': 2.683}, {'end': 810.198, 'text': 'So we saw them in computer vision and NLP and speech and translation in RL and so on.', 'start': 804.352, 'duration': 5.846}, {'end': 813.822, 'text': 'So everyone started to use the same kind of modeling tool, model framework.', 'start': 810.518, 'duration': 3.304}], 'summary': 'Neural nets scale well, seen in various ai areas since 2012.', 'duration': 25.293, 'max_score': 788.529, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E788529.jpg'}, {'end': 874.361, 'src': 'embed', 'start': 841.051, 'weight': 1, 'content': [{'end': 843.812, 'text': "it's not even that just the toolkits and the neural networks were similar.", 'start': 841.051, 'duration': 2.761}, {'end': 849.614, 'text': 'is that literally the architectures converged to, like one architecture that you copy paste across everything seemingly?', 'start': 843.812, 'duration': 5.802}, {'end': 855.036, 'text': 'So this was kind of an unassuming machine translation paper at the time proposing the transformer architecture.', 'start': 850.314, 'duration': 4.722}, {'end': 861.418, 'text': 'But what we found since then is that you can just basically copy paste this architecture and use it everywhere.', 'start': 855.156, 'duration': 6.262}, {'end': 866.1, 'text': "And what's changing is the details of the data and the chunking of the data and how you feed them.", 'start': 861.698, 'duration': 4.402}, {'end': 869.616, 'text': "And, you know, that's a caricature, but it's kind of like a correct first order statement.", 'start': 866.753, 'duration': 2.863}, {'end': 874.361, 'text': "And so now papers are even more similar looking because everyone's just using transformer.", 'start': 870.117, 'duration': 4.244}], 'summary': 'Transformer architecture allows easy replication across various applications due to its adaptable nature.', 'duration': 33.31, 'max_score': 841.051, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E841051.jpg'}], 'start': 649.973, 'title': 'Evolution of ai and neural networks', 'summary': 'Discusses the evolution of ai from machine learning to deep learning, the cumbersome process of image classification in 2011, and the convergence of neural network architectures, scaling impact on large datasets, and the role of transformer architecture in unifying machine learning models, leading to potential convergence toward a uniform learning algorithm.', 'chapters': [{'end': 724.728, 'start': 649.973, 'title': 'Evolution of ai and computer vision', 'summary': 'Discusses the evolution of ai from machine learning to deep learning, the transition in the perception of ai, and the cumbersome process of image classification in 2011, highlighting the complexity of feature extraction and classification.', 'duration': 74.755, 'highlights': ["The transition in the perception of AI In 2012, AI was seen as a 'dirty word,' but it has now become acceptable, marking a shift in the perception of AI.", 'Complexity of image classification in 2011 In 2011, image classification involved extracting and utilizing a myriad of features and descriptors, leading to a cumbersome and complicated process.', 'Evolution from machine learning to deep learning The speaker joined AI in 2012 when it was predominantly referred to as machine learning, signifying the evolution to the current prominence of deep learning in the field of AI.']}, {'end': 1095.962, 'start': 724.788, 'title': 'Evolution of neural networks in ai', 'summary': 'Traces the evolution of neural networks in ai, highlighting the convergence of architectures, the impact of scaling neural networks on large data sets, and the role of the transformer architecture in unifying machine learning models, leading to increased similarity in papers and potential convergence toward a uniform learning algorithm.', 'duration': 371.174, 'highlights': ['The convergence of architectures and the widespread adoption of transformer architecture have led to increased similarity in papers and the potential convergence toward a uniform learning algorithm. Increased similarity in papers, potential convergence toward a uniform learning algorithm', 'The scaling of large neural networks on large data sets has demonstrated strong performance, leading to the widespread use of neural networks across different areas of AI since 2012. Widespread use of neural networks across different areas of AI since 2012', 'The introduction of the transformer architecture in 2017 has led to its widespread adoption and the tendency to use it across different areas, resulting in an increase in the similarity of papers utilizing this architecture. Widespread adoption of transformer architecture, increase in the similarity of papers utilizing this architecture']}], 'duration': 445.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E649973.jpg', 'highlights': ['The scaling of large neural networks on large data sets has demonstrated strong performance, leading to the widespread use of neural networks across different areas of AI since 2012.', 'The introduction of the transformer architecture in 2017 has led to its widespread adoption and the tendency to use it across different areas, resulting in an increase in the similarity of papers utilizing this architecture.', 'The convergence of architectures and the widespread adoption of transformer architecture have led to increased similarity in papers and the potential convergence toward a uniform learning algorithm.', 'Evolution from machine learning to deep learning The speaker joined AI in 2012 when it was predominantly referred to as machine learning, signifying the evolution to the current prominence of deep learning in the field of AI.', 'Complexity of image classification in 2011 In 2011, image classification involved extracting and utilizing a myriad of features and descriptors, leading to a cumbersome and complicated process.', "The transition in the perception of AI In 2012, AI was seen as a 'dirty word,' but it has now become acceptable, marking a shift in the perception of AI."]}, {'end': 1801.631, 'segs': [{'end': 1133.141, 'src': 'embed', 'start': 1095.962, 'weight': 1, 'content': [{'end': 1103.404, 'text': 'and so this is the first time that really you start to like look at it, and this is the current modern equations of the attention,', 'start': 1095.962, 'duration': 7.442}, {'end': 1105.144, 'text': 'and I think this was the first paper that I saw it in.', 'start': 1103.404, 'duration': 1.74}, {'end': 1111.372, 'text': "It's the first time that there's a word attention used, as far as I know, to call this mechanism.", 'start': 1105.549, 'duration': 5.823}, {'end': 1115.233, 'text': 'So I actually tried to dig into the details of the history of the attention.', 'start': 1112.092, 'duration': 3.141}, {'end': 1119.835, 'text': 'So the first author here, Dimitri, I had an email correspondence with him.', 'start': 1115.794, 'duration': 4.041}, {'end': 1121.396, 'text': 'And I basically sent him an email.', 'start': 1120.175, 'duration': 1.221}, {'end': 1122.757, 'text': "I'm like, Dimitri, this is really interesting.", 'start': 1121.416, 'duration': 1.341}, {'end': 1124.097, 'text': 'Transformers have taken over.', 'start': 1122.777, 'duration': 1.32}, {'end': 1128.019, 'text': 'Where did you come up with the soft attention mechanism that ends up being the heart of the transformer?', 'start': 1124.397, 'duration': 3.622}, {'end': 1133.141, 'text': 'And to my surprise, he wrote me back this massive email, which was really fascinating.', 'start': 1128.539, 'duration': 4.602}], 'summary': 'First paper to introduce attention mechanism in modern equations, with email correspondence with dimitri, the first author.', 'duration': 37.179, 'max_score': 1095.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1095962.jpg'}, {'end': 1226.891, 'src': 'embed', 'start': 1200.929, 'weight': 0, 'content': [{'end': 1207.238, 'text': 'would have been called R&N Search or just, But we have Yoshua Bengio to thank for a little bit of better name, I would say.', 'start': 1200.929, 'duration': 6.309}, {'end': 1209.78, 'text': "So apparently, that's the history of the subject.", 'start': 1207.659, 'duration': 2.121}, {'end': 1210.4, 'text': 'That was interesting.', 'start': 1209.8, 'duration': 0.6}, {'end': 1214.643, 'text': 'OK, so that brings us to 2017, which is attention is all you need.', 'start': 1211.581, 'duration': 3.062}, {'end': 1219.306, 'text': "So this attention component, which in Dimitri's paper was just like one small segment.", 'start': 1214.983, 'duration': 4.323}, {'end': 1222.168, 'text': "And there's all this bi-directional RNN, RNN and decoder.", 'start': 1219.426, 'duration': 2.742}, {'end': 1226.891, 'text': 'And this attention only paper is saying, OK, you can actually delete everything.', 'start': 1222.709, 'duration': 4.182}], 'summary': "Yoshua bengio named r&n search, attention in 2017 revolutionized with 'attention is all you need'", 'duration': 25.962, 'max_score': 1200.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1200929.jpg'}, {'end': 1331.794, 'src': 'embed', 'start': 1303.937, 'weight': 3, 'content': [{'end': 1307.619, 'text': "And I believe there's a number of papers that try to play with all kinds of little details of the transformer.", 'start': 1303.937, 'duration': 3.682}, {'end': 1311.141, 'text': 'And nothing sticks, because this is actually quite good.', 'start': 1307.739, 'duration': 3.402}, {'end': 1319.787, 'text': "The only thing, to my knowledge, that didn't stick was this reshuffling of the layer norms to go into the pre-norm version, where here you see,", 'start': 1311.161, 'duration': 8.626}, {'end': 1322.748, 'text': 'the layer norms are, after the multi-headed attention, repeat forward.', 'start': 1319.787, 'duration': 2.961}, {'end': 1324.149, 'text': 'They just put them before instead.', 'start': 1322.768, 'duration': 1.381}, {'end': 1326.15, 'text': 'So just reshuffling of layer norms.', 'start': 1324.63, 'duration': 1.52}, {'end': 1331.794, 'text': "But otherwise, the GPTs and everything else that you're seeing today is basically the 2017 architecture from five years ago.", 'start': 1326.191, 'duration': 5.603}], 'summary': "Transformer's architecture is solid, with only one unsuccessful change in layer norms.", 'duration': 27.857, 'max_score': 1303.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1303937.jpg'}, {'end': 1597.853, 'src': 'heatmap', 'start': 1364.882, 'weight': 4, 'content': [{'end': 1366.603, 'text': 'so let me try a different way.', 'start': 1364.882, 'duration': 1.721}, {'end': 1368.485, 'text': 'um, of like how i see it.', 'start': 1366.603, 'duration': 1.882}, {'end': 1374.769, 'text': 'basically, to me, attention is kind of like the communication phase of the transformer, and the transformer interleaves two phases uh,', 'start': 1368.485, 'duration': 6.284}, {'end': 1381.693, 'text': 'the communication phase, which is the multi-headed attention, and the computation stage, which is, uh, the small paleo perceptron or p2.', 'start': 1374.769, 'duration': 6.924}, {'end': 1388.988, 'text': "so in the communication phase, uh, it's really just a data dependent message, passing on directed graphs, And you can think of it as OK,", 'start': 1381.693, 'duration': 7.295}, {'end': 1391.208, 'text': 'forget everything with machine translation and everything.', 'start': 1388.988, 'duration': 2.22}, {'end': 1394.37, 'text': "Let's just, we have directed graphs at each node.", 'start': 1391.228, 'duration': 3.142}, {'end': 1395.47, 'text': 'You are storing a vector.', 'start': 1394.49, 'duration': 0.98}, {'end': 1401.572, 'text': 'And then let me talk now about the communication phase of how these vectors talk to each other in this directed graph.', 'start': 1396.21, 'duration': 5.362}, {'end': 1408.474, 'text': 'And then the compute phase later is just a multi-layer of a perceptron, which then basically acts on every node individually.', 'start': 1401.892, 'duration': 6.582}, {'end': 1412.316, 'text': 'But how do these nodes talk to each other in this directed graph?', 'start': 1409.075, 'duration': 3.241}, {'end': 1414.597, 'text': 'So I wrote some simple Python.', 'start': 1412.956, 'duration': 1.641}, {'end': 1425.766, 'text': 'I wrote this in Python, basically to create one round of communication, using attention as the message passing scheme.', 'start': 1417, 'duration': 8.766}, {'end': 1430.709, 'text': 'So here, a node has this private data vector.', 'start': 1426.607, 'duration': 4.102}, {'end': 1433.651, 'text': 'You can think of it as private information to this node.', 'start': 1430.729, 'duration': 2.922}, {'end': 1437.334, 'text': 'And then it can also emit a key, a query, and a value.', 'start': 1434.272, 'duration': 3.062}, {'end': 1440.976, 'text': "And simply, that's done by a linear transformation from this node.", 'start': 1437.774, 'duration': 3.202}, {'end': 1449.935, 'text': "So the key is, what are the things that I sorry, the query is one of the things that I'm looking for.", 'start': 1441.416, 'duration': 8.519}, {'end': 1454.298, 'text': 'The key is one of the things that I have, and the value is one of the things that I will communicate.', 'start': 1450.276, 'duration': 4.022}, {'end': 1460.921, 'text': "And so then, when you have your graph that's made up of nodes and some random edges, when you actually have these nodes communicating,", 'start': 1455.478, 'duration': 5.443}, {'end': 1469.005, 'text': "what's happening is you loop over all the nodes individually in some random order and you're at some node and you get the query vector Q,", 'start': 1460.921, 'duration': 8.084}, {'end': 1471.406, 'text': "which is I'm a node in some graph.", 'start': 1469.005, 'duration': 2.401}, {'end': 1473.487, 'text': "and this is what I'm looking for.", 'start': 1471.406, 'duration': 2.081}, {'end': 1476.208, 'text': "And so that's just achieved via this linear transformation here.", 'start': 1473.967, 'duration': 2.241}, {'end': 1483.182, 'text': 'And then we look at all the inputs that point to this node, and then they broadcast where are the things that I have, which is their keys.', 'start': 1477.017, 'duration': 6.165}, {'end': 1485.584, 'text': 'So they broadcast the keys.', 'start': 1484.223, 'duration': 1.361}, {'end': 1487.005, 'text': 'I have the query.', 'start': 1486.244, 'duration': 0.761}, {'end': 1490.487, 'text': 'Then those interact by dot product to get scores.', 'start': 1487.565, 'duration': 2.922}, {'end': 1493.47, 'text': 'So, basically, simply by doing dot product,', 'start': 1491.248, 'duration': 2.222}, {'end': 1501.956, 'text': "you get some kind of an unnormalized weighting of the interestingness of all of the information in the nodes that point to me and to the things I'm looking for.", 'start': 1493.47, 'duration': 8.486}, {'end': 1506.153, 'text': 'And then, when you normalize that with softmax, so it just sums to one,', 'start': 1502.711, 'duration': 3.442}, {'end': 1510.716, 'text': 'you basically just end up using those scores which now sum to one in our probability distribution,', 'start': 1506.153, 'duration': 4.563}, {'end': 1514.498, 'text': 'and you do a weighted sum of the values to get your update.', 'start': 1510.716, 'duration': 3.782}, {'end': 1517.48, 'text': 'So I have a query.', 'start': 1515.279, 'duration': 2.201}, {'end': 1524.704, 'text': 'they have keys, dot, product to get interestingness or, like affinity, softmax to normalize it and then weighted,', 'start': 1517.48, 'duration': 7.224}, {'end': 1527.206, 'text': 'sum of those values flow to me and update me.', 'start': 1524.704, 'duration': 2.502}, {'end': 1530.688, 'text': 'And this is happening for each node individually, and then we update at the end.', 'start': 1528.066, 'duration': 2.622}, {'end': 1540.469, 'text': 'And so this kind of a message passing scheme is kind of like at the heart of the transformer and happens in the more vectorized, batched way.', 'start': 1531.28, 'duration': 9.189}, {'end': 1547.156, 'text': 'that is more confusing and is also interspersed with, interspersed with layer norms and things like that to make the training behave better.', 'start': 1540.469, 'duration': 6.687}, {'end': 1551.22, 'text': "But that's roughly what's happening in the attention mechanism, I think, on the high level.", 'start': 1547.937, 'duration': 3.283}, {'end': 1558.687, 'text': 'So, Yeah, so, in the communication phase of the transformer,', 'start': 1551.24, 'duration': 7.447}, {'end': 1567.95, 'text': 'then this message passing scheme happens in every head in parallel and then in every layer in series and with different weights each time.', 'start': 1558.687, 'duration': 9.263}, {'end': 1572.371, 'text': "And that's it as far as the multi-headed attention goes.", 'start': 1568.63, 'duration': 3.741}, {'end': 1576.853, 'text': 'And so, if you look at these encoder-decoder models, you can sort of think of it.', 'start': 1573.292, 'duration': 3.561}, {'end': 1580.994, 'text': 'then, in terms of the connectivity of these nodes in the graph, you can kind of think of it as like okay,', 'start': 1576.853, 'duration': 4.141}, {'end': 1585.747, 'text': 'all these tokens that are in the encoder that we want to condition on, they are fully connected to each other.', 'start': 1580.994, 'duration': 4.753}, {'end': 1589.909, 'text': 'So when they communicate, they communicate fully when you calculate their features.', 'start': 1586.207, 'duration': 3.702}, {'end': 1593.851, 'text': 'But in the decoder because we are trying to have a language model.', 'start': 1590.589, 'duration': 3.262}, {'end': 1597.853, 'text': "we don't want to have communication from future tokens because they give away the answer at this step.", 'start': 1593.851, 'duration': 4.002}], 'summary': "Attention is like message passing in transformer's communication phase, involving vectors and directed graphs.", 'duration': 232.971, 'max_score': 1364.882, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1364882.jpg'}, {'end': 1801.631, 'src': 'embed', 'start': 1759.475, 'weight': 5, 'content': [{'end': 1763.937, 'text': 'cross-attention and self-attention only differ in where the keys and the values come from.', 'start': 1759.475, 'duration': 4.462}, {'end': 1772.14, 'text': 'Either the keys and values are produced from this node, or they are produced from some external source, like an encoder and the nodes over there.', 'start': 1764.277, 'duration': 7.863}, {'end': 1777.062, 'text': "But algorithmically, it's the same mathematical operations.", 'start': 1773.301, 'duration': 3.761}, {'end': 1801.631, 'text': 'So think of each one of these nodes as a token.', 'start': 1796.828, 'duration': 4.803}], 'summary': 'Cross-attention and self-attention differ in key and value sources, but share same operations.', 'duration': 42.156, 'max_score': 1759.475, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1759475.jpg'}], 'start': 1095.962, 'title': "Evolution and architecture of transformer's attention mechanism", 'summary': "Explores the evolution of attention mechanism in transformers, from its origin as a soft attention mechanism to its pivotal role in 'attention is all you need' in 2017, along with the 2017 transformer architecture, highlighting the resilience of attention mechanism with multiple heads and layer norms, and explaining the parallel nature of multi-headed attention and the distinction between self-attention and cross-attention.", 'chapters': [{'end': 1257.547, 'start': 1095.962, 'title': 'Evolution of attention mechanism in transformers', 'summary': "Discusses the evolution of the attention mechanism in transformers, from its origins as a soft attention mechanism proposed by dimitri to its pivotal role in the landmark paper 'attention is all you need' in 2017, which demonstrated the potential of attention-only models.", 'duration': 161.585, 'highlights': ["The name 'attention' came from Yoshua Bengio on one of the final passes as they went over the paper, leading to the landmark paper 'Attention is All You Need' in 2017. The naming of 'attention' by Yoshua Bengio in the paper 'Attention is All You Need' was a pivotal moment that highlighted the importance of the attention mechanism in transformers.", "The paper 'Attention is All You Need' demonstrated the potential of attention-only models, achieving a very good local minimum in the architecture space. The paper 'Attention is All You Need' represented a landmark in the evolution of attention mechanisms, demonstrating the effectiveness of attention-only models and achieving a significant milestone in the architecture space.", "The soft attention mechanism proposed by Dimitri ended up being the heart of the transformer, leading to the development of attention-only models. Dimitri's soft attention mechanism ended up being the fundamental component of the transformer, paving the way for the development of attention-only models."]}, {'end': 1801.631, 'start': 1258.877, 'title': 'Transformer architecture and attention mechanism', 'summary': "Discusses the 2017 transformer architecture, highlighting the use of attention mechanism with multiple heads and layer norms, which has proven resilient despite attempts at modification, and explains the communication and computation phases of the transformer's attention mechanism, emphasizing the parallel nature of multi-headed attention and the distinction between self-attention and cross-attention.", 'duration': 542.754, 'highlights': ['The 2017 transformer architecture with attention mechanism and layer norms has proven remarkably resilient, with widely used hyperparameters. The 2017 transformer architecture, incorporating attention mechanism with multiple heads and layer norms, has demonstrated resilience and widespread adoption of its hyperparameters.', 'The communication and computation phases of the attention mechanism in the transformer are explained, emphasizing the parallel nature of multi-headed attention. The communication and computation phases of the attention mechanism in the transformer are detailed, highlighting the parallel nature of multi-headed attention.', 'Distinguishing between self-attention and cross-attention, the chapter clarifies that they differ in the source of keys and values, with both employing similar mathematical operations. Self-attention and cross-attention are distinguished by the source of keys and values, with both utilizing similar mathematical operations.']}], 'duration': 705.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1095962.jpg', 'highlights': ["The naming of 'attention' by Yoshua Bengio in the paper 'Attention is All You Need' was a pivotal moment that highlighted the importance of the attention mechanism in transformers.", "The paper 'Attention is All You Need' represented a landmark in the evolution of attention mechanisms, demonstrating the effectiveness of attention-only models and achieving a significant milestone in the architecture space.", "Dimitri's soft attention mechanism ended up being the fundamental component of the transformer, paving the way for the development of attention-only models.", 'The 2017 transformer architecture, incorporating attention mechanism with multiple heads and layer norms, has demonstrated resilience and widespread adoption of its hyperparameters.', 'The communication and computation phases of the attention mechanism in the transformer are detailed, highlighting the parallel nature of multi-headed attention.', 'Self-attention and cross-attention are distinguished by the source of keys and values, with both utilizing similar mathematical operations.']}, {'end': 2343.751, 'segs': [{'end': 1901.252, 'src': 'embed', 'start': 1872.478, 'weight': 0, 'content': [{'end': 1875.28, 'text': "So it's a pretty serious implementation that reproduces GPT-2, I would say.", 'start': 1872.478, 'duration': 2.802}, {'end': 1877.624, 'text': 'and provided enough compute.', 'start': 1876.022, 'duration': 1.602}, {'end': 1882.391, 'text': 'This was one node of eight GPUs for 38 hours or something like that, if I remember correctly.', 'start': 1878.125, 'duration': 4.266}, {'end': 1883.753, 'text': "And it's very readable.", 'start': 1882.912, 'duration': 0.841}, {'end': 1885.896, 'text': "It's 300 lives, so everyone can take a look at it.", 'start': 1883.793, 'duration': 2.103}, {'end': 1889.641, 'text': 'And yeah, let me basically briefly step through it.', 'start': 1887.378, 'duration': 2.263}, {'end': 1893.949, 'text': "So let's try to have a decoder-only transformer.", 'start': 1891.007, 'duration': 2.942}, {'end': 1896.01, 'text': "So what that means is that it's a language model.", 'start': 1894.509, 'duration': 1.501}, {'end': 1901.252, 'text': 'It tries to model the next word in the sequence or the next character in the sequence.', 'start': 1896.13, 'duration': 5.122}], 'summary': 'Implemented gpt-2 on one node with 8 gpus for 38 hours, achieving a readable language model with 300 lives.', 'duration': 28.774, 'max_score': 1872.478, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1872478.jpg'}, {'end': 1940.907, 'src': 'embed', 'start': 1912.738, 'weight': 2, 'content': [{'end': 1915.279, 'text': "You take all of Shakespeare, concatenate it, and it's one megabyte file.", 'start': 1912.738, 'duration': 2.541}, {'end': 1919.461, 'text': 'And then you can train language models on it and get infinite Shakespeare, if you like, which I think is kind of cool.', 'start': 1915.559, 'duration': 3.902}, {'end': 1920.622, 'text': 'So we have a text.', 'start': 1920.102, 'duration': 0.52}, {'end': 1924.132, 'text': 'The first thing we need to do is we need to convert it to a sequence of integers.', 'start': 1921.209, 'duration': 2.923}, {'end': 1930.638, 'text': "Because transformers natively process, you know, you can't plug text into transformers.", 'start': 1925.213, 'duration': 5.425}, {'end': 1931.658, 'text': 'You need to somehow encode it.', 'start': 1930.658, 'duration': 1}, {'end': 1936.703, 'text': 'So the way that encoding is done is we convert, for example, in the simplest case, every character gets an integer.', 'start': 1932.039, 'duration': 4.664}, {'end': 1940.907, 'text': 'And then instead of hi there, we would have this sequence of integers.', 'start': 1937.764, 'duration': 3.143}], 'summary': "Shakespeare's complete works fit into a 1mb file. text is converted to integers for processing by transformers.", 'duration': 28.169, 'max_score': 1912.738, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1912738.jpg'}, {'end': 1980.064, 'src': 'heatmap', 'start': 1932.039, 'weight': 0.809, 'content': [{'end': 1936.703, 'text': 'So the way that encoding is done is we convert, for example, in the simplest case, every character gets an integer.', 'start': 1932.039, 'duration': 4.664}, {'end': 1940.907, 'text': 'And then instead of hi there, we would have this sequence of integers.', 'start': 1937.764, 'duration': 3.143}, {'end': 1947.249, 'text': 'So then you can encode every single character as an integer and get like a massive sequence of integers.', 'start': 1941.926, 'duration': 5.323}, {'end': 1952.611, 'text': 'You just concatenate it all into one large, long, one dimensional sequence, and then you can train on it.', 'start': 1947.329, 'duration': 5.282}, {'end': 1954.652, 'text': 'Now, here we only have a single document.', 'start': 1953.291, 'duration': 1.361}, {'end': 1957.373, 'text': 'In some cases, if you have multiple independent documents,', 'start': 1955.012, 'duration': 2.361}, {'end': 1965.057, 'text': 'what people like to do is create special tokens and they intersperse those documents with those special end of text tokens that they splice in between to create boundaries.', 'start': 1957.373, 'duration': 7.684}, {'end': 1971.279, 'text': "But those boundaries actually don't have any any modeling impact.", 'start': 1966.318, 'duration': 4.961}, {'end': 1980.064, 'text': "it's just that the transformer is supposed to learn via back propagation that the end of document sequence means that you should wipe the memory.", 'start': 1971.279, 'duration': 8.785}], 'summary': 'Encoding involves converting characters to integers, concatenating into one sequence for training, and using special tokens to create document boundaries.', 'duration': 48.025, 'max_score': 1932.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1932039.jpg'}, {'end': 2099.842, 'src': 'embed', 'start': 2074.717, 'weight': 3, 'content': [{'end': 2081.942, 'text': "It's just that the context grows linearly for the predictions that you make along the T direction in the model.", 'start': 2074.717, 'duration': 7.225}, {'end': 2087.005, 'text': 'So this is all the examples that the model will learn from this single batch.', 'start': 2082.822, 'duration': 4.183}, {'end': 2091.896, 'text': 'So now this is the GPT class.', 'start': 2089.454, 'duration': 2.442}, {'end': 2099.842, 'text': "And because this is a decoder-only model, so we're not going to have an encoder because there's no English we're translating from.", 'start': 2093.277, 'duration': 6.565}], 'summary': 'Model learns from linearly growing context for predictions along t direction in decoder-only gpt class.', 'duration': 25.125, 'max_score': 2074.717, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2074717.jpg'}, {'end': 2269.909, 'src': 'embed', 'start': 2231.476, 'weight': 4, 'content': [{'end': 2235.098, 'text': "um, there's again, as i mentioned, this communicate phase and the compute phase.", 'start': 2231.476, 'duration': 3.622}, {'end': 2243.621, 'text': 'so in the communicate phase all the nodes get to talk to each other, and so these nodes are basically if our block size is eight,', 'start': 2235.098, 'duration': 8.523}, {'end': 2245.682, 'text': 'then we are going to have eight nodes in this graph.', 'start': 2243.621, 'duration': 2.061}, {'end': 2247.756, 'text': "There's eight nodes in this graph.", 'start': 2246.735, 'duration': 1.021}, {'end': 2249.757, 'text': 'The first node is pointed to only by itself.', 'start': 2248.036, 'duration': 1.721}, {'end': 2253.059, 'text': 'The second node is pointed to by the first node and itself.', 'start': 2250.417, 'duration': 2.642}, {'end': 2256.781, 'text': 'The third node is pointed to by the first two nodes and itself, et cetera.', 'start': 2253.779, 'duration': 3.002}, {'end': 2257.701, 'text': "So there's eight nodes here.", 'start': 2256.801, 'duration': 0.9}, {'end': 2262.664, 'text': 'So you apply the residual pathway in X.', 'start': 2259.122, 'duration': 3.542}, {'end': 2263.325, 'text': 'You take it out.', 'start': 2262.664, 'duration': 0.661}, {'end': 2267.647, 'text': 'You apply a layer norm and then the self-attention so that these communicate, these eight nodes communicate.', 'start': 2263.465, 'duration': 4.182}, {'end': 2269.909, 'text': 'But you have to keep in mind that the batch is four.', 'start': 2268.048, 'duration': 1.861}], 'summary': 'In the graph with 8 nodes, communication occurs in 2 phases - 4 nodes communicate in each batch.', 'duration': 38.433, 'max_score': 2231.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2231476.jpg'}], 'start': 1804.472, 'title': 'Transformer implementation and language modeling', 'summary': "Discusses the implementation of a decoder-only transformer, particularly nanogpt, replicating gpt-2 with a minimal approach, requiring eight gpus and 38 hours of compute time, and being 300 lines long and easily readable. it also covers training a language model using a transformer, using shakespeare's text as an example, and explains the process of encoding, batching, and the architecture of the gpt class, including the blocks of the transformer such as communicate and compute phases, and the causal self-attention part.", 'chapters': [{'end': 1893.949, 'start': 1804.472, 'title': 'Understanding transformer implementation', 'summary': 'Discusses the implementation of a decoder-only transformer, particularly nanogpt, replicating gpt-2 with a minimal approach, requiring eight gpus and 38 hours of compute time, and being 300 lines long and easily readable.', 'duration': 89.477, 'highlights': ['NanoGPT is a minimal implementation that replicates GPT-2, requiring eight GPUs and 38 hours of compute time.', 'The implementation is 300 lines long and easily readable.', 'The chapter discusses the implementation of a decoder-only transformer, particularly NanoGPT, replicating GPT-2 with a minimal approach.']}, {'end': 2343.751, 'start': 1894.509, 'title': 'Language modeling and transformer training', 'summary': "Discusses training a language model using a transformer, using shakespeare's text as an example, and explains the process of encoding, batching, and the architecture of the gpt class. it also delves into the blocks of the transformer, including the communicate and compute phases, and the causal self-attention part.", 'duration': 449.242, 'highlights': ['The process involves training a language model using a transformer on a one-megabyte file containing concatenated Shakespearean text, creating sequences of integers from the text, and producing batches for parallel processing, with the aim of fully utilizing GPU parallelism and optimizing batch size.', 'The architecture of the GPT class is discussed, focusing on the decoding process and the use of a linear layer for generating the probability distribution for the next character in the sequence, along with the application of cross-entropy loss for training.', 'The chapter provides insights into the architecture of the transformer, including the application of residual pathways, layer normalization, self-attention, and multi-headed attention, as well as the use of a multi-layer perceptron (MLP) with a gelu non-linearity for individual processing on each node.', 'The communicate and compute phases of the transformer blocks are explained, detailing the process of nodes communicating among themselves, the application of the Motallio perceptron, and the presence of the causal self-attention part, which is identified as the most crucial and complex component.']}], 'duration': 539.279, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E1804472.jpg', 'highlights': ['The implementation of NanoGPT replicates GPT-2 with a minimal approach, requiring eight GPUs and 38 hours of compute time.', 'The implementation is 300 lines long and easily readable.', 'The process involves training a language model using a transformer on a one-megabyte file containing concatenated Shakespearean text, creating sequences of integers from the text, and producing batches for parallel processing.', 'The architecture of the GPT class is discussed, focusing on the decoding process and the use of a linear layer for generating the probability distribution for the next character in the sequence.', 'The chapter provides insights into the architecture of the transformer, including the application of residual pathways, layer normalization, self-attention, and multi-headed attention.', 'The communicate and compute phases of the transformer blocks are explained, detailing the process of nodes communicating among themselves and the application of the Motallio perceptron.']}, {'end': 2825.695, 'segs': [{'end': 2418.751, 'src': 'embed', 'start': 2380.41, 'weight': 0, 'content': [{'end': 2381.691, 'text': "So that's why this is all tricky.", 'start': 2380.41, 'duration': 1.281}, {'end': 2390.778, 'text': 'But basically, in the forward pass, we are calculating the queries, keys, and values based on x.', 'start': 2381.731, 'duration': 9.047}, {'end': 2392.119, 'text': 'So these are the keys, queries, and values.', 'start': 2390.778, 'duration': 1.341}, {'end': 2398.043, 'text': "Here, when I'm computing the attention, I have the queries matrix multiplying the keys.", 'start': 2392.739, 'duration': 5.304}, {'end': 2402.667, 'text': 'So this is the dot product in parallel for all the queries and all the keys and all the heads.', 'start': 2398.223, 'duration': 4.444}, {'end': 2408.847, 'text': "So I failed to mention that there's also the aspect of the heads, which is also done all in parallel here.", 'start': 2403.584, 'duration': 5.263}, {'end': 2412.008, 'text': 'So we have the batch dimension, the time dimension, and the head dimension.', 'start': 2409.467, 'duration': 2.541}, {'end': 2413.489, 'text': 'And you end up with five-dimensional tensors.', 'start': 2412.088, 'duration': 1.401}, {'end': 2414.449, 'text': "And it's all really confusing.", 'start': 2413.529, 'duration': 0.92}, {'end': 2418.751, 'text': 'So I invite you to step through it later and convince yourself that this is actually doing the right thing.', 'start': 2414.809, 'duration': 3.942}], 'summary': 'In the forward pass, queries, keys, and values are computed based on x, leading to five-dimensional tensors and parallel dot products for all the queries and keys.', 'duration': 38.341, 'max_score': 2380.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2380410.jpg'}, {'end': 2504.862, 'src': 'embed', 'start': 2479.174, 'weight': 3, 'content': [{'end': 2485.276, 'text': "And then transpose contiguous view, because it's all complicated and bashed in five-dimensional tensors, but it's really not doing anything.", 'start': 2479.174, 'duration': 6.102}, {'end': 2489.757, 'text': 'Optional dropout, and then a linear projection back to the residual pathway.', 'start': 2485.756, 'duration': 4.001}, {'end': 2492.955, 'text': 'So this is implementing the communication phase here.', 'start': 2490.754, 'duration': 2.201}, {'end': 2500.94, 'text': 'Then you can train this transformer and then you can generate infinite Shakespeare.', 'start': 2494.676, 'duration': 6.264}, {'end': 2504.862, 'text': 'And we will simply do this by because our block size is eight.', 'start': 2501.32, 'duration': 3.542}], 'summary': 'Implementing transformer for generating shakespeare with block size of eight', 'duration': 25.688, 'max_score': 2479.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2479174.jpg'}, {'end': 2583.578, 'src': 'embed', 'start': 2557.824, 'weight': 6, 'content': [{'end': 2563.227, 'text': 'And then if you want to generate beyond eight, you have to start cropping because the transformer only works for eight elements in time dimension.', 'start': 2557.824, 'duration': 5.403}, {'end': 2570.172, 'text': 'And so all of these transformers in the Naive setting have a finite block size or context length.', 'start': 2563.888, 'duration': 6.284}, {'end': 2576.136, 'text': 'And in typical models, this will be 1, 024 tokens or 2, 048 tokens, something like that.', 'start': 2570.834, 'duration': 5.302}, {'end': 2580.537, 'text': 'But these tokens are usually like DPE tokens or sentence piece tokens or workpiece tokens.', 'start': 2576.456, 'duration': 4.081}, {'end': 2581.577, 'text': "There's many different encodings.", 'start': 2580.597, 'duration': 0.98}, {'end': 2583.578, 'text': "So it's not like that long.", 'start': 2582.618, 'duration': 0.96}], 'summary': 'Transformers have finite block size, usually 1,024 or 2,048 tokens.', 'duration': 25.754, 'max_score': 2557.824, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2557824.jpg'}, {'end': 2717.377, 'src': 'embed', 'start': 2691.911, 'weight': 5, 'content': [{'end': 2697.693, 'text': 'You can have an encoder only model like BERT, or you can have an encoder decoder model like say T5 and things like machine translation.', 'start': 2691.911, 'duration': 5.782}, {'end': 2705.808, 'text': "So and in BERT, you can't train it using sort of this language modeling setup that's autoregressive.", 'start': 2698.402, 'duration': 7.406}, {'end': 2707.61, 'text': "And you're just trying to predict the next sequence.", 'start': 2705.868, 'duration': 1.742}, {'end': 2709.431, 'text': "You're training it with slightly different objectives.", 'start': 2707.85, 'duration': 1.581}, {'end': 2714.515, 'text': "You're putting in like the full sentence and the full sentence is allowed to communicate fully.", 'start': 2709.811, 'duration': 4.704}, {'end': 2717.377, 'text': "And then you're trying to classify sentiment or something like that.", 'start': 2714.996, 'duration': 2.381}], 'summary': 'Bert is an encoder-only model, t5 is an encoder-decoder model for tasks like machine translation, sentiment classification.', 'duration': 25.466, 'max_score': 2691.911, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2691911.jpg'}], 'start': 2343.751, 'title': 'Transformer architecture', 'summary': 'Delves into attention mechanism, graph connectivity masking, and transformer architecture, focusing on dimensions, tensors, and training objectives for models like gpt and bert.', 'chapters': [{'end': 2437.896, 'start': 2343.751, 'title': 'Attention mechanism and masking in graph connectivity', 'summary': 'Explains the complexity of the attention mechanism and graph connectivity masking in the forward pass, involving the calculation of queries, keys, and values, with a focus on the batch, time, and head dimensions, resulting in five-dimensional tensors and a masked fill operation.', 'duration': 94.145, 'highlights': ['The complexity of the attention mechanism and graph connectivity masking in the forward pass involves calculating queries, keys, and values based on x, with a focus on the batch, time, and head dimensions, resulting in five-dimensional tensors and a masked fill operation.', 'In the forward pass, when computing the attention, the queries matrix multiplies the keys using dot product in parallel for all the queries, keys, and heads.', 'The batch, time, and head dimensions are crucial in the evaluation of features for all batch, head, and time elements, resulting in the generation of five-dimensional tensors.']}, {'end': 2825.695, 'start': 2438.516, 'title': 'Understanding transformer architecture', 'summary': 'Explains the implementation of the transformer architecture, including attention mechanism, training process, and context limitations, with a focus on encoder, decoder, and cross-attention. it also highlights the differences in training objectives for models like gpt and bert.', 'duration': 387.179, 'highlights': ['The chapter explains the implementation of the transformer architecture, including attention mechanism, training process, and context limitations. ', 'It details the process of communication and information gathering within the transformer architecture, including the use of attention, dropout, matrix multiplication, and linear projection. ', 'The chapter discusses the limitations of context length in transformer models, noting the finite block size and typical context lengths of 1,024 or 2,048 tokens. context lengths of 1,024 or 2,048 tokens', 'It explains the differences in training objectives for models like GPT and BERT, highlighting the autoregressive language modeling setup for GPT and the use of full sentence communication for BERT. ']}], 'duration': 481.944, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2343751.jpg', 'highlights': ['The complexity of the attention mechanism and graph connectivity masking in the forward pass involves calculating queries, keys, and values based on x, with a focus on the batch, time, and head dimensions, resulting in five-dimensional tensors and a masked fill operation.', 'The batch, time, and head dimensions are crucial in the evaluation of features for all batch, head, and time elements, resulting in the generation of five-dimensional tensors.', 'In the forward pass, when computing the attention, the queries matrix multiplies the keys using dot product in parallel for all the queries, keys, and heads.', 'The chapter explains the implementation of the transformer architecture, including attention mechanism, training process, and context limitations.', 'It details the process of communication and information gathering within the transformer architecture, including the use of attention, dropout, matrix multiplication, and linear projection.', 'It explains the differences in training objectives for models like GPT and BERT, highlighting the autoregressive language modeling setup for GPT and the use of full sentence communication for BERT.', 'The chapter discusses the limitations of context length in transformer models, noting the finite block size and typical context lengths of 1,024 or 2,048 tokens.']}, {'end': 3396.891, 'segs': [{'end': 2865.698, 'src': 'embed', 'start': 2826.035, 'weight': 2, 'content': [{'end': 2830.777, 'text': "If you have an encoder and you're training a BERT, you have how many tokens you want, and they are fully connected.", 'start': 2826.035, 'duration': 4.742}, {'end': 2834.758, 'text': 'And if you have a decoder-only model, you have this triangular thing.', 'start': 2831.717, 'duration': 3.041}, {'end': 2839.94, 'text': 'And if you have encoder-decoder, then you have awkwardly sort of like two pools of nodes.', 'start': 2835.078, 'duration': 4.862}, {'end': 2844.862, 'text': 'Yeah Go ahead.', 'start': 2840.16, 'duration': 4.702}, {'end': 2865.698, 'text': "I guess I think there's like a My question is, I wonder if you know much more about this than I know actually.", 'start': 2845.622, 'duration': 20.076}], 'summary': 'Discussion about encoder-decoder models and their structure.', 'duration': 39.663, 'max_score': 2826.035, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2826035.jpg'}, {'end': 3269.355, 'src': 'heatmap', 'start': 3222.362, 'weight': 0.79, 'content': [{'end': 3227.826, 'text': 'Also in speech recognition, you just take your MEL spectrogram and you chop it up into little slices and feed them into a transformer.', 'start': 3222.362, 'duration': 5.464}, {'end': 3229.728, 'text': 'So there was paper like this, but also Whisper.', 'start': 3228.127, 'duration': 1.601}, {'end': 3231.53, 'text': 'Whisper is a copy-based transformer.', 'start': 3230.028, 'duration': 1.502}, {'end': 3234.172, 'text': 'If you saw Whisper from OpenAI,', 'start': 3231.79, 'duration': 2.382}, {'end': 3239.817, 'text': "you just chop up MEL spectrogram and feed it into a transformer and then pretend you're dealing with text and it works very well.", 'start': 3234.172, 'duration': 5.645}, {'end': 3242.368, 'text': 'Decision transformer and RL.', 'start': 3240.888, 'duration': 1.48}, {'end': 3249.31, 'text': "you take your states actions and reward that you experience in the environment and you just pretend it's a language and you start to model the sequences of that.", 'start': 3242.368, 'duration': 6.942}, {'end': 3251.371, 'text': 'And then you can use that for planning later.', 'start': 3249.67, 'duration': 1.701}, {'end': 3252.311, 'text': 'That works pretty well.', 'start': 3251.691, 'duration': 0.62}, {'end': 3254.271, 'text': 'Even things like alpha fold.', 'start': 3253.331, 'duration': 0.94}, {'end': 3257.292, 'text': 'So we were briefly talking about molecules and how you can plug them in.', 'start': 3254.311, 'duration': 2.981}, {'end': 3260.493, 'text': 'So at the heart of alpha fold computationally is also a transformer.', 'start': 3257.652, 'duration': 2.841}, {'end': 3267.074, 'text': "One thing I wanted to also say about transformers is I find that they're super flexible, and I really enjoy that.", 'start': 3262.373, 'duration': 4.701}, {'end': 3269.355, 'text': "I'll give you an example from Tesla.", 'start': 3268.234, 'duration': 1.121}], 'summary': 'Mel spectrogram chops into slices, feeds into transformer, works well in speech recognition and decision making, flexible and used in alpha fold and tesla.', 'duration': 46.993, 'max_score': 3222.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3222362.jpg'}, {'end': 3283.298, 'src': 'embed', 'start': 3253.331, 'weight': 1, 'content': [{'end': 3254.271, 'text': 'Even things like alpha fold.', 'start': 3253.331, 'duration': 0.94}, {'end': 3257.292, 'text': 'So we were briefly talking about molecules and how you can plug them in.', 'start': 3254.311, 'duration': 2.981}, {'end': 3260.493, 'text': 'So at the heart of alpha fold computationally is also a transformer.', 'start': 3257.652, 'duration': 2.841}, {'end': 3267.074, 'text': "One thing I wanted to also say about transformers is I find that they're super flexible, and I really enjoy that.", 'start': 3262.373, 'duration': 4.701}, {'end': 3269.355, 'text': "I'll give you an example from Tesla.", 'start': 3268.234, 'duration': 1.121}, {'end': 3273.83, 'text': 'Like, you have a comnet that takes an image and makes predictions about the image.', 'start': 3271.127, 'duration': 2.703}, {'end': 3278.394, 'text': "And then the big question is, how do you feed in extra information? And it's not always trivial.", 'start': 3274.51, 'duration': 3.884}, {'end': 3283.298, 'text': 'Like, say I have additional information that I want to inform, that I want the outputs to be informed by.', 'start': 3278.414, 'duration': 4.884}], 'summary': 'Alpha fold uses a transformer algorithm, which is highly flexible and enables feeding of additional information for enhanced predictions.', 'duration': 29.967, 'max_score': 3253.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3253331.jpg'}, {'end': 3359.844, 'src': 'embed', 'start': 3325.893, 'weight': 0, 'content': [{'end': 3329.075, 'text': 'The compute actually kind of happens in almost like 3D space, if you think about it.', 'start': 3325.893, 'duration': 3.182}, {'end': 3331.517, 'text': 'But in intention, everything is just sets.', 'start': 3330.056, 'duration': 1.461}, {'end': 3337.261, 'text': "So it's a very flexible framework, and you can just throw in stuff into your conditioning set and everything just self-attended over.", 'start': 3332.097, 'duration': 5.164}, {'end': 3339.643, 'text': "It's quite beautiful, as I expected.", 'start': 3338.061, 'duration': 1.582}, {'end': 3342.587, 'text': 'Okay, so now what exactly makes Transformers so effective?', 'start': 3339.924, 'duration': 2.663}, {'end': 3347.921, 'text': 'I think a good example of this comes from the GPT-3 paper, which I encourage people to read.', 'start': 3343.82, 'duration': 4.101}, {'end': 3349.901, 'text': 'Language models are two-shot learners.', 'start': 3348.561, 'duration': 1.34}, {'end': 3352.402, 'text': 'I would have probably renamed this a little bit.', 'start': 3350.522, 'duration': 1.88}, {'end': 3357.503, 'text': 'I would have said something like, transformers are capable of in-context learning or meta-learning.', 'start': 3352.422, 'duration': 5.081}, {'end': 3359.844, 'text': "That's kind of what makes them really special.", 'start': 3357.803, 'duration': 2.041}], 'summary': 'Transformers operate in a flexible 3d space, enabling effective in-context learning.', 'duration': 33.951, 'max_score': 3325.893, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3325893.jpg'}], 'start': 2826.035, 'title': 'Transformers in ml', 'summary': 'Discusses the architecture of encoder-decoder models, bert training, and the impact of transformers in ml. it mentions fully connected tokens, the triangular structure for decoder-only model, and examples of transformer applications in various fields.', 'chapters': [{'end': 2899.628, 'start': 2826.035, 'title': 'Encoder-decoder model and bert training', 'summary': 'Discusses the architecture of encoder-decoder models and bert training, mentioning fully connected tokens, triangular structure for decoder-only model, and the complexity of running patient studies.', 'duration': 73.593, 'highlights': ['The encoder and BERT training involves fully connected tokens.', 'The decoder-only model has a triangular structure.', 'Running patient studies is complex and challenging.']}, {'end': 3396.891, 'start': 2899.628, 'title': 'Understanding transformers in ml', 'summary': 'Discusses the evolution and impact of transformers in machine learning, highlighting their flexibility and effectiveness, with examples of their applications in various fields like computer vision, speech recognition, and alphafold.', 'duration': 497.263, 'highlights': ["Transformers' flexibility and effectiveness Transformers are hailed for their flexibility and effectiveness in machine learning, enabling easy integration of additional information and self-attention for communication, freeing neural nets from the constraints of Euclidean space.", "Applications of transformers in various fields The transcript provides examples of transformers' applications in diverse fields, such as using transformers for computer vision by chopping up images into squares, utilizing them for speech recognition through MEL spectrogram slices, and employing them in AlphaFold for molecular analysis.", "Meta-learning capability of transformers The discussion delves into the meta-learning capability of transformers, exemplified by the GPT-3 paper, which showcases transformers' in-context learning and their ability to improve accuracy with more examples given in the context."]}], 'duration': 570.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E2826035.jpg', 'highlights': ["Transformers' flexibility and effectiveness in machine learning, enabling easy integration of additional information and self-attention for communication, freeing neural nets from the constraints of Euclidean space.", 'Applications of transformers in diverse fields, such as using transformers for computer vision, speech recognition, and molecular analysis.', 'The encoder and BERT training involves fully connected tokens.', 'The decoder-only model has a triangular structure.', 'The discussion delves into the meta-learning capability of transformers, exemplified by the GPT-3 paper.']}, {'end': 4293.731, 'segs': [{'end': 3548.647, 'src': 'embed', 'start': 3522.267, 'weight': 1, 'content': [{'end': 3526.03, 'text': 'Number two, it is very optimisable, thanks to things like residual connections, layer nodes and so on.', 'start': 3522.267, 'duration': 3.763}, {'end': 3527.831, 'text': "And number three, it's extremely efficient.", 'start': 3526.49, 'duration': 1.341}, {'end': 3532.875, 'text': 'This is not always appreciated, but the transformer, if you look at the computational graph, is a shallow,', 'start': 3527.951, 'duration': 4.924}, {'end': 3536.077, 'text': 'wide network which is perfect to take advantage of the parallelism of GPUs.', 'start': 3532.875, 'duration': 3.202}, {'end': 3540.08, 'text': 'So I think the transformer was designed very deliberately to run efficiently on GPUs.', 'start': 3536.477, 'duration': 3.603}, {'end': 3544.723, 'text': "There's previous work like Neural GPU that I really enjoy as well.", 'start': 3540.7, 'duration': 4.023}, {'end': 3548.647, 'text': 'Which is really just like how do we design neural nets that are efficient on GPUs?', 'start': 3545.205, 'duration': 3.442}], 'summary': 'The transformer is highly optimizable and efficient, designed to run efficiently on gpus.', 'duration': 26.38, 'max_score': 3522.267, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3522267.jpg'}, {'end': 3716.16, 'src': 'embed', 'start': 3656.838, 'weight': 0, 'content': [{'end': 3660.039, 'text': 'And instead of performing a single fixed sequence, you can design the sequence in the prompt.', 'start': 3656.838, 'duration': 3.201}, {'end': 3665.441, 'text': 'And because the transformer is both powerful but also is trained on large enough, very hard data set,', 'start': 3660.559, 'duration': 4.882}, {'end': 3667.282, 'text': 'it kind of becomes this general purpose text computer.', 'start': 3665.441, 'duration': 1.841}, {'end': 3669.122, 'text': "And so I think that's kind of interesting to look at it.", 'start': 3667.662, 'duration': 1.46}, {'end': 3672.643, 'text': 'Yeah Yeah.', 'start': 3671.223, 'duration': 1.42}, {'end': 3682.416, 'text': 'Yes Yes.', 'start': 3682.076, 'duration': 0.34}, {'end': 3716.16, 'text': "how much do you think it's like?", 'start': 3713.899, 'duration': 2.261}], 'summary': 'Transformer model can be designed and trained on large data for general text computation.', 'duration': 59.322, 'max_score': 3656.838, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3656838.jpg'}, {'end': 3863.043, 'src': 'embed', 'start': 3837.889, 'weight': 2, 'content': [{'end': 3843.253, 'text': 'And I think number three is less talked about, but extremely important because in deep learning, scale matters.', 'start': 3837.889, 'duration': 5.364}, {'end': 3847.956, 'text': 'And so the size of the network that you can train gives you is extremely important.', 'start': 3843.733, 'duration': 4.223}, {'end': 3851.318, 'text': "And so if it's efficient on the current hardware, then we can make it bigger.", 'start': 3848.557, 'duration': 2.761}, {'end': 3860.625, 'text': "We mentioned that if you're dealing with multiple technologies, you need to feed it all together.", 'start': 3851.338, 'duration': 9.287}, {'end': 3863.043, 'text': 'How does that actually work??', 'start': 3862.062, 'duration': 0.981}], 'summary': 'In deep learning, scale matters for network training efficiency and hardware compatibility.', 'duration': 25.154, 'max_score': 3837.889, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3837889.jpg'}, {'end': 4033.618, 'src': 'embed', 'start': 4007.777, 'weight': 3, 'content': [{'end': 4013.101, 'text': 'And maybe if you have a much smaller data set, then maybe convolutions are a good idea because you actually have this bias coming from the filters.', 'start': 4007.777, 'duration': 5.324}, {'end': 4022.59, 'text': 'but i think, um so, the transformer is extremely general, but there are ways to mess with the encodings, to put in more structure, like you could,', 'start': 4015.285, 'duration': 7.305}, {'end': 4025.532, 'text': 'for example, encode sinuses and cosines and fix it.', 'start': 4022.59, 'duration': 2.942}, {'end': 4031.297, 'text': 'or you could actually go to the attention mechanism and say okay, if my, if my image is chopped up into patches,', 'start': 4025.532, 'duration': 5.765}, {'end': 4033.618, 'text': 'this patch can only communicate to this neighborhood and you can.', 'start': 4031.297, 'duration': 2.321}], 'summary': 'Using transformers can be beneficial for general data but may require adjustments for smaller data sets.', 'duration': 25.841, 'max_score': 4007.777, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E4007777.jpg'}, {'end': 4102.163, 'src': 'embed', 'start': 4062.822, 'weight': 4, 'content': [{'end': 4064.482, 'text': 'And they are factored out in the positional encodings.', 'start': 4062.822, 'duration': 1.66}, {'end': 4070.444, 'text': 'And you can mess with this per computation.', 'start': 4065.043, 'duration': 5.401}, {'end': 4086.272, 'text': "So there's probably about 200 papers on this now, if not more.", 'start': 4082.669, 'duration': 3.603}, {'end': 4088.053, 'text': "They're kind of hard to keep track of.", 'start': 4086.912, 'duration': 1.141}, {'end': 4092.837, 'text': 'Honestly, like my Safari browser, which is on my computer, like 200 open tabs.', 'start': 4088.093, 'duration': 4.744}, {'end': 4102.163, 'text': "But yes, I'm not even sure if I want to pick my favorite, honestly.", 'start': 4093.857, 'duration': 8.306}], 'summary': 'Approximately 200 papers on positional encodings, hard to keep track.', 'duration': 39.341, 'max_score': 4062.822, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E4062822.jpg'}, {'end': 4185.621, 'src': 'embed', 'start': 4158.173, 'weight': 5, 'content': [{'end': 4162.496, 'text': 'you will save whatever it puts in there in a external thing and allow it to attend over it.', 'start': 4158.173, 'duration': 4.323}, {'end': 4167.099, 'text': "So basically, you can teach the transformer just dynamically because it's so meta-learned.", 'start': 4163.196, 'duration': 3.903}, {'end': 4172.402, 'text': 'You can teach it dynamically to use other gizmos and gadgets and allow it to expand its memory that way, if that makes sense.', 'start': 4167.618, 'duration': 4.784}, {'end': 4175.724, 'text': "It's just like human learning to use a notepad.", 'start': 4172.803, 'duration': 2.921}, {'end': 4177.104, 'text': "You don't have to keep it in your brain.", 'start': 4176.064, 'duration': 1.04}, {'end': 4179.607, 'text': 'So keeping things in your brain is kind of like the context line for the transformer.', 'start': 4177.265, 'duration': 2.342}, {'end': 4185.621, 'text': 'but maybe we can just give it a notebook and then it can query the notebook and read from it and write to it.', 'start': 4180.1, 'duration': 5.521}], 'summary': 'The transformer can dynamically learn to use external tools, expanding its memory like humans using notepads.', 'duration': 27.448, 'max_score': 4158.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E4158173.jpg'}, {'end': 4284.423, 'src': 'embed', 'start': 4259.32, 'weight': 6, 'content': [{'end': 4265.023, 'text': "I mean I'm going basically slightly from computer vision and kind of like computer vision-based products to a little bit in the language domain.", 'start': 4259.32, 'duration': 5.703}, {'end': 4267.024, 'text': "Where's NanoGPT? Okay, NanoGPT.", 'start': 4265.583, 'duration': 1.441}, {'end': 4270.212, 'text': 'So originally, I had MinGPT, which I rewrote to NanoGPT.', 'start': 4267.97, 'duration': 2.242}, {'end': 4271.753, 'text': "And I'm working on this.", 'start': 4270.832, 'duration': 0.921}, {'end': 4272.894, 'text': "I'm trying to reproduce GPTs.", 'start': 4271.773, 'duration': 1.121}, {'end': 4279.499, 'text': 'And I mean, I think something like ChatGPT, I think, incrementally improved in a product fashion would be extremely interesting.', 'start': 4273.174, 'duration': 6.325}, {'end': 4284.423, 'text': "And I think a lot of people feel it, and that's why it went so wide.", 'start': 4280.12, 'duration': 4.303}], 'summary': 'Working on nanogpt, aiming to improve chatgpt for product use.', 'duration': 25.103, 'max_score': 4259.32, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E4259320.jpg'}], 'start': 3397.392, 'title': 'Transformers in nlp and cv', 'summary': "Discusses the transformer's inner loop learning for gradient-based learning, its potential as a general-purpose text computer, and the use of positional encoding in nlp and cv, handling biases, and memory expansion in transformer networks, with reference to over 200 papers.", 'chapters': [{'end': 3860.625, 'start': 3397.392, 'title': "Transformer's inner loop learning", 'summary': "Discusses the transformer's ability to potentially perform gradient-based learning within its activations, leading to efficient, optimizable, and expressive functions, as well as its potential as a general-purpose text computer.", 'duration': 463.233, 'highlights': ["The transformer potentially performs gradient-based learning within its activations, making it an efficient, optimizable, and expressive text computer. The transformer's ability to perform gradient-based learning within its activations makes it efficient, optimizable, and expressive, potentially enabling meta-learning and implementing functions like ridge regression. This aligns with the transformer's design for efficiency on GPUs and its potential as a general-purpose text computer.", "The transformer's design enables efficient processing of inputs, with shallow, wide graphs that facilitate easy gradient flow and parallel processing. The transformer's design as a shallow, wide graph facilitates easy gradient flow and parallel processing, allowing for efficient input processing. This contrasts with RNNs, which have long, thin compute graphs, hindering optimizability and efficiency.", "Efficiency in deep learning is crucial, and the transformer's design allows for scalability, making it important for training large networks. The transformer's efficiency on current hardware is crucial for scalability in deep learning, enabling the training of large networks, which is essential for its effectiveness."]}, {'end': 4293.731, 'start': 3862.062, 'title': 'Transformers and positional encoding', 'summary': 'Discusses the use of transformers in natural language processing and computer vision, including the use of positional encoding, the handling of biases, and the potential for memory expansion in transformer networks, with a mention of over 200 papers on the topic.', 'duration': 431.669, 'highlights': ['The use of transformers in natural language processing and computer vision is discussed, with a focus on positional encoding and the handling of biases, and the potential for memory expansion in transformer networks. (Relevance: 5)', 'There is a mention of over 200 papers on the topic of transformers, indicating the extensive research and development in this area. (Relevance: 4)', "The potential for memory expansion in transformer networks through the use of a 'scratch pad' is explained, drawing parallels to human learning using a notepad. (Relevance: 3)", 'The speaker mentions the transition from working on computer vision-based products to delving into the language domain, particularly focusing on NanoGPT and ChatGPT. (Relevance: 2)']}], 'duration': 896.339, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XfpMkf4rD6E/pics/XfpMkf4rD6E3397392.jpg', 'highlights': ['The transformer potentially performs gradient-based learning within its activations, making it an efficient, optimizable, and expressive text computer.', "The transformer's design enables efficient processing of inputs, with shallow, wide graphs that facilitate easy gradient flow and parallel processing.", "Efficiency in deep learning is crucial, and the transformer's design allows for scalability, making it important for training large networks.", 'The use of transformers in natural language processing and computer vision is discussed, with a focus on positional encoding and the handling of biases, and the potential for memory expansion in transformer networks.', 'There is a mention of over 200 papers on the topic of transformers, indicating the extensive research and development in this area.', "The potential for memory expansion in transformer networks through the use of a 'scratch pad' is explained, drawing parallels to human learning using a notepad.", 'The speaker mentions the transition from working on computer vision-based products to delving into the language domain, particularly focusing on NanoGPT and ChatGPT.']}], 'highlights': ["Transformers' successes in 2021 include solving long sequence problems, zero-shot generalization, and multimodal tasks, and unique applications in audio, art, music, and storytelling.", 'The course covers the revolutionary impact of deep learning models like transformers in various fields such as natural language processing, computer vision, reinforcement learning, biology, and robotics.', 'The instructors, including a PhD student and a first-year CSB student, share their backgrounds and research interests in robotics, autonomous driving, personal learning, computer vision, NLP, and music.', "The naming of 'attention' by Yoshua Bengio in the paper 'Attention is All You Need' was a pivotal moment that highlighted the importance of the attention mechanism in transformers.", "The paper 'Attention is All You Need' represented a landmark in the evolution of attention mechanisms, demonstrating the effectiveness of attention-only models and achieving a significant milestone in the architecture space.", 'The transformer potentially performs gradient-based learning within its activations, making it an efficient, optimizable, and expressive text computer.', 'The scaling of large neural networks on large data sets has demonstrated strong performance, leading to the widespread use of neural networks across different areas of AI since 2012.', 'The introduction of the transformer architecture in 2017 has led to its widespread adoption and the tendency to use it across different areas, resulting in an increase in the similarity of papers utilizing this architecture.', 'The communication and computation phases of the attention mechanism in the transformer are detailed, highlighting the parallel nature of multi-headed attention.', 'The implementation of NanoGPT replicates GPT-2 with a minimal approach, requiring eight GPUs and 38 hours of compute time.']}