title
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

description
Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time. NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests: The Chain Rule: https://youtu.be/wl1myxrtQHQ Gradient Descent: https://youtu.be/sDv4f4s2SB8 Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4 Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8 Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo If you're interested in learning more about the SoftMax function, check out: https://youtu.be/KpKog-L9veg If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0 If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out: Cosine Similarity: https://youtu.be/e9U0QAFbfLI Attention: https://youtu.be/PSs6nxngL6k If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! paypal: https://www.paypal.me/statquest venmo: @JoshStarmer Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 0:00 Awesome song and introduction 1:26 Word Embedding 7:30 Positional Encoding 12:53 Self-Attention 23:37 Encoder and Decoder defined 23:53 Decoder Word Embedding 25:08 Decoder Positional Encoding 25:50 Transformers were designed for parallel computing 27:13 Decoder Self-Attention 27:59 Encoder-Decoder Attention 31:19 Decoding numbers into words 32:23 Decoding the second token 34:13 Extra stuff you can add to a Transformer #StatQuest #Transformer #ChatGPT

detail
{'title': "Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!", 'heatmap': [{'end': 549.628, 'start': 516.658, 'weight': 0.811}, {'end': 979.625, 'start': 864.215, 'weight': 1}, {'end': 1153.669, 'start': 1108.542, 'weight': 0.89}, {'end': 1370.611, 'start': 1279.912, 'weight': 0.858}, {'end': 1830.04, 'start': 1756.461, 'weight': 0.708}, {'end': 1921.68, 'start': 1864.827, 'weight': 0.777}], 'summary': "Explains the usage of transformer neural networks in translating english to spanish, word embedding using activation functions, positional encoding, self-attention mechanism, parallel computation, encoder-decoder attention, and neural machine translation process, providing a comprehensive understanding of chatgpt's foundation.", 'chapters': [{'end': 136.535, 'segs': [{'end': 30.753, 'src': 'embed', 'start': 0.589, 'weight': 1, 'content': [{'end': 7.415, 'text': "Translation, it's done with a Transformer, StatQuest.", 'start': 0.589, 'duration': 6.826}, {'end': 11.678, 'text': "Hello, I'm Josh Starmer and welcome to StatQuest.", 'start': 8.215, 'duration': 3.463}, {'end': 17.342, 'text': "Today we're going to talk about Transformer neural networks, and they're going to be clearly explained.", 'start': 12.078, 'duration': 5.264}, {'end': 23.647, 'text': 'Transformers are more fun when you build them in the cloud with lightning.', 'start': 17.903, 'duration': 5.744}, {'end': 30.753, 'text': 'Bam Right now, people are going bonkers about something called ChatGPT.', 'start': 24.447, 'duration': 6.306}], 'summary': 'Transformer neural networks explained in statquest by josh starmer.', 'duration': 30.164, 'max_score': 0.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY589.jpg'}, {'end': 112.704, 'src': 'embed', 'start': 66.323, 'weight': 0, 'content': [{'end': 77.087, 'text': "Specifically, we're going to focus on how a transformer neural network can translate a simple English sentence, let's go, into Spanish, vamos.", 'start': 66.323, 'duration': 10.764}, {'end': 86.03, 'text': 'Now, since a transformer is a type of neural network and neural networks usually only have numbers for input values,', 'start': 78.027, 'duration': 8.003}, {'end': 92.012, 'text': 'the first thing we need to do is find a way to turn the input and output words into numbers.', 'start': 86.03, 'duration': 5.982}, {'end': 101.157, 'text': 'There are a lot of ways to convert words into numbers but for neural networks one of the most commonly used methods is called word embedding.', 'start': 92.952, 'duration': 8.205}, {'end': 112.704, 'text': 'The main idea of word embedding is to use a relatively simple neural network that has one input for every word and symbol in the vocabulary that you want to use.', 'start': 101.858, 'duration': 10.846}], 'summary': "Transformer neural network translates english 'let's go' to spanish 'vamos'.", 'duration': 46.381, 'max_score': 66.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY66323.jpg'}], 'start': 0.589, 'title': 'Transformer neural networks', 'summary': 'Explains the usage of a transformer neural network in translating a simple english sentence into spanish using word embedding and the concept of a token. it also introduces the application of transformer in chatgpt.', 'chapters': [{'end': 136.535, 'start': 0.589, 'title': 'Transformer neural networks', 'summary': 'Explains how a transformer neural network translates a simple english sentence into spanish using word embedding and the concept of a token, and introduces the usage of transformer in chatgpt.', 'duration': 135.946, 'highlights': ['Transformers are more fun when you build them in the cloud with lightning, and people are excited about something called ChatGPT, which is fundamentally based on a Transformer.', 'The chapter focuses on how a transformer neural network can translate a simple English sentence into Spanish, using word embedding and tokens.', 'Word embedding is a commonly used method for converting words into numbers for neural networks, using a simple neural network with one input for every word and symbol in the vocabulary.']}], 'duration': 135.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY589.jpg', 'highlights': ['The chapter focuses on how a transformer neural network can translate a simple English sentence into Spanish, using word embedding and tokens.', 'Transformers are more fun when you build them in the cloud with lightning, and people are excited about something called ChatGPT, which is fundamentally based on a Transformer.', 'Word embedding is a commonly used method for converting words into numbers for neural networks, using a simple neural network with one input for every word and symbol in the vocabulary.']}, {'end': 438.659, 'segs': [{'end': 276.287, 'src': 'embed', 'start': 252.236, 'weight': 2, 'content': [{'end': 260.901, 'text': 'In this example the activation functions themselves are just identity functions meaning the output values are the same as the input values.', 'start': 252.236, 'duration': 8.665}, {'end': 271.744, 'text': 'In other words, if the input value or x-axis coordinate for the activation function on the left is 1.87,, then the output value,', 'start': 261.778, 'duration': 9.966}, {'end': 276.287, 'text': 'the y-axis coordinate, will also be 1.87..', 'start': 271.744, 'duration': 4.543}], 'summary': 'Activation functions in this example are identity functions with output values equal to input values.', 'duration': 24.051, 'max_score': 252.236, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY252236.jpg'}, {'end': 363.784, 'src': 'embed', 'start': 335.511, 'weight': 1, 'content': [{'end': 340.454, 'text': 'First, we reuse the same word embedding network for each input word or symbol.', 'start': 335.511, 'duration': 4.943}, {'end': 348.531, 'text': 'In other words, the weights in the network for lets are the exact same as the weights in the network for go.', 'start': 341.385, 'duration': 7.146}, {'end': 358.599, 'text': 'This means that regardless of how long the input sentence is, we just copy and use the exact same word embedding network for each word or symbol.', 'start': 349.692, 'duration': 8.907}, {'end': 363.784, 'text': 'And this gives us flexibility to handle input sentences with different lengths.', 'start': 359.48, 'duration': 4.304}], 'summary': 'Word embedding network is reused for each input word or symbol, providing flexibility for handling input sentences with different lengths.', 'duration': 28.273, 'max_score': 335.511, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY335511.jpg'}, {'end': 438.659, 'src': 'embed', 'start': 410.529, 'weight': 0, 'content': [{'end': 417.011, 'text': 'But when we train the transformer with English phrases and known Spanish translations,', 'start': 410.529, 'duration': 6.482}, {'end': 423.253, 'text': 'backpropagation optimizes these values one step at a time and results in these final weights.', 'start': 417.011, 'duration': 6.242}, {'end': 430.056, 'text': 'Also, just to be clear, the process of optimizing the weights is also called training.', 'start': 424.314, 'duration': 5.742}, {'end': 438.659, 'text': "Bam! Note, there is a lot more to be said about training and backpropagation, so if you're interested, check out the quests.", 'start': 430.756, 'duration': 7.903}], 'summary': 'Training the transformer with english phrases and known spanish translations using backpropagation optimizes the weights, resulting in final weights.', 'duration': 28.13, 'max_score': 410.529, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY410529.jpg'}], 'start': 137.475, 'title': 'Word embedding and activation functions', 'summary': 'Discusses word embedding using activation functions and weights, where input values are multiplied by weights to generate output values representing words, and how back propagation optimizes the weights in the network.', 'chapters': [{'end': 438.659, 'start': 137.475, 'title': 'Word embedding and activation functions', 'summary': 'Explains the process of word embedding using activation functions and weights, where input values are multiplied by weights to generate output values representing words, and how back propagation optimizes the weights in the network.', 'duration': 301.184, 'highlights': ["The process of word embedding involves multiplying input values by weights to obtain output values that represent words, such as 1.87 and 0.09 for the word 'Let's'.", 'The activation functions used are identity functions, where the output values are the same as the input values.', 'Back propagation is utilized to optimize the weights in the word embedding network, gradually adjusting them to find the optimal values, a process also referred to as training.']}], 'duration': 301.184, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY137475.jpg', 'highlights': ['Back propagation optimizes weights in word embedding network.', 'Word embedding involves multiplying input values by weights to obtain output values representing words.', 'Activation functions used are identity functions, where output values are the same as input values.']}, {'end': 766.288, 'segs': [{'end': 549.628, 'src': 'heatmap', 'start': 472.195, 'weight': 0, 'content': [{'end': 482.539, 'text': 'So these two phrases, Squatch eats pizza and pizza eats Squatch, use the exact same words but have very different meanings.', 'start': 472.195, 'duration': 10.344}, {'end': 486.92, 'text': 'So keeping track of word order is super important.', 'start': 483.419, 'duration': 3.501}, {'end': 493.323, 'text': "So let's talk about positional encoding, which is a technique that transformers use to keep track of word order.", 'start': 487.601, 'duration': 5.722}, {'end': 500.689, 'text': "We'll start by showing how to add positional encoding to the first phrase, Squatch Eats Pizza.", 'start': 494.585, 'duration': 6.104}, {'end': 507.933, 'text': "Note, there are a bunch of ways to do positional encoding, but we're just going to talk about one popular method.", 'start': 501.789, 'duration': 6.144}, {'end': 515.636, 'text': 'That said, the first thing we do is convert the words Squatch Eats Pizza into numbers using word embedding.', 'start': 508.833, 'duration': 6.803}, {'end': 522.582, 'text': "In this example, we've got a new vocabulary and we're creating four word embedding values per word.", 'start': 516.658, 'duration': 5.924}, {'end': 529.885, 'text': 'However, in practice people often create hundreds or even thousands of embedding values per word.', 'start': 523.618, 'duration': 6.267}, {'end': 536.672, 'text': 'Now we add a set of numbers that correspond to word order to the embedding values for each word.', 'start': 531.066, 'duration': 5.606}, {'end': 541.737, 'text': 'Hey, Josh, where do the numbers that correspond to word order come from?', 'start': 536.692, 'duration': 5.045}, {'end': 549.628, 'text': 'In this case, the numbers that represent the word order come from a sequence of alternating sine and cosine squiggles.', 'start': 542.662, 'duration': 6.966}], 'summary': 'Positional encoding helps transformers track word order, using sine and cosine squiggles.', 'duration': 50.387, 'max_score': 472.195, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY472195.jpg'}, {'end': 678.226, 'src': 'embed', 'start': 655.976, 'weight': 3, 'content': [{'end': 665.56, 'text': 'However, because the squiggles get wider for larger embedding positions, and the more embedding values we have, then, the wider the squiggles get.', 'start': 655.976, 'duration': 9.584}, {'end': 672.463, 'text': 'then, even with a repeat value here and there, we end up with a unique sequence of position values for each word.', 'start': 665.56, 'duration': 6.903}, {'end': 678.226, 'text': 'Thus, each input word ends up with a unique sequence of position values.', 'start': 673.624, 'duration': 4.602}], 'summary': 'Wider squiggles for larger embedding positions create unique sequence of position values for each word.', 'duration': 22.25, 'max_score': 655.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY655976.jpg'}, {'end': 736.156, 'src': 'embed', 'start': 709.984, 'weight': 2, 'content': [{'end': 717.761, 'text': 'And when we add the positional values to the embeddings, we end up with new positional encoding for the first and third words.', 'start': 709.984, 'duration': 7.777}, {'end': 721.844, 'text': "And the second word, since it didn't move, stays the same.", 'start': 718.501, 'duration': 3.343}, {'end': 727.229, 'text': 'Thus, positional encoding allows a transformer to keep track of word order.', 'start': 722.705, 'duration': 4.524}, {'end': 736.156, 'text': "Bam! Now let's go back to our simple example, where we are just trying to translate the English sentence, let's go.", 'start': 727.929, 'duration': 8.227}], 'summary': 'Adding positional values to embeddings allows a transformer to keep track of word order.', 'duration': 26.172, 'max_score': 709.984, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY709984.jpg'}], 'start': 439.801, 'title': 'Word order and positional encoding', 'summary': 'Emphasizes the significance of word order in natural language processing and introduces positional encoding as a technique used by transformers to keep track of word order. it delves into the process of adding positional encoding to word embeddings, utilizing sine and cosine functions to generate unique position values for each word.', 'chapters': [{'end': 522.582, 'start': 439.801, 'title': 'Positional encoding in word order', 'summary': 'Explains the importance of word order in natural language processing and introduces positional encoding as a technique used by transformers to keep track of word order, with a focus on converting words into numbers and adding positional encoding to phrases.', 'duration': 82.781, 'highlights': ["The importance of word order in natural language processing is highlighted by the example of two phrases, 'Squatch eats pizza' and 'pizza eats Squatch', which use the exact same words but convey very different meanings. The example of 'Squatch eats pizza' and 'pizza eats Squatch' demonstrates the significance of word order in conveying different meanings through the same words.", "Explanation of positional encoding as a technique used by transformers to keep track of word order and its introduction for adding positional encoding to the phrase 'Squatch Eats Pizza'. The chapter introduces positional encoding as a technique employed by transformers to maintain word order and demonstrates its application to the phrase 'Squatch Eats Pizza'.", 'Introduction to word embedding and its conversion of words into numbers using a new vocabulary with four word embedding values per word. The chapter introduces word embedding and its function of converting words into numbers using a new vocabulary with four word embedding values per word.']}, {'end': 766.288, 'start': 523.618, 'title': 'Word embedding positional encoding', 'summary': 'Discusses the process of adding positional encoding to word embeddings, utilizing sine and cosine squiggles to generate unique position values for each word, enabling a transformer to maintain word order and handle sentence reordering.', 'duration': 242.67, 'highlights': ['The process of adding positional encoding to word embeddings is discussed, using sine and cosine squiggles to generate unique position values for each word. Positional encoding allows a transformer to keep track of word order and handle sentence reordering, ensuring unique sequence of position values for each word.', 'The method of obtaining position values for each word through the y-axis coordinates on the squiggles is explained, with the wider squiggles providing unique sequence of position values for each word despite occasional repeat values. The wider squiggles for larger embedding positions ensure unique sequence of position values for each word, even with occasional repeat values, ensuring a unique sequence of position values for each word.', 'The detailed process of obtaining position values for specific words in a sentence and the impact of sentence reordering on positional encoding is described. Sentence reordering affects the positional encoding of specific words, with the positional values for the first and third words changing when the word order is reversed, while the second word remains unchanged.']}], 'duration': 326.487, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY439801.jpg', 'highlights': ["The example of 'Squatch eats pizza' and 'pizza eats Squatch' demonstrates the significance of word order in conveying different meanings through the same words.", "The chapter introduces positional encoding as a technique employed by transformers to maintain word order and demonstrates its application to the phrase 'Squatch Eats Pizza'.", 'Positional encoding allows a transformer to keep track of word order and handle sentence reordering, ensuring unique sequence of position values for each word.', 'The wider squiggles for larger embedding positions ensure unique sequence of position values for each word, even with occasional repeat values, ensuring a unique sequence of position values for each word.', 'Sentence reordering affects the positional encoding of specific words, with the positional values for the first and third words changing when the word order is reversed, while the second word remains unchanged.', 'The chapter introduces word embedding and its function of converting words into numbers using a new vocabulary with four word embedding values per word.']}, {'end': 1059.642, 'segs': [{'end': 979.625, 'src': 'heatmap', 'start': 813.776, 'weight': 0, 'content': [{'end': 822.241, 'text': 'The good news is that transformers have something called self-attention, which is a mechanism to correctly associate the word it with the word pizza.', 'start': 813.776, 'duration': 8.465}, {'end': 831.415, 'text': 'In general terms, self-attention works by seeing how similar each word is to all of the words in the sentence, including itself.', 'start': 823.248, 'duration': 8.167}, {'end': 841.663, 'text': 'For example, self-attention calculates the similarity between the first word, the, and all of the words in the sentence, including itself.', 'start': 832.115, 'duration': 9.548}, {'end': 846.927, 'text': 'And self-attention calculates these similarities for every word in the sentence.', 'start': 842.643, 'duration': 4.284}, {'end': 854.31, 'text': 'Once the similarities are calculated, they are used to determine how the transformer encodes each word.', 'start': 848.268, 'duration': 6.042}, {'end': 864.215, 'text': 'For example, if you looked at a lot of sentences about pizza and the word it was more commonly associated with pizza than oven,', 'start': 855.291, 'duration': 8.924}, {'end': 871.517, 'text': 'then the similarity score for pizza will cause it to have a larger impact on how the word it is encoded by the transformer.', 'start': 864.215, 'duration': 7.302}, {'end': 879.067, 'text': "Bam Now that we know the main ideas of how self-attention works, let's look at the details.", 'start': 872.418, 'duration': 6.649}, {'end': 886.359, 'text': "So let's go back to our simple example, where we had just added positional encoding to the words let's and go.", 'start': 879.768, 'duration': 6.591}, {'end': 893.449, 'text': "The first thing we do is multiply the position encoded values for the word let's by a pair of weights.", 'start': 887.523, 'duration': 5.926}, {'end': 898.955, 'text': 'And we add those products together to get negative 1.0.', 'start': 894.23, 'duration': 4.725}, {'end': 904.841, 'text': 'Then we do the same thing with a different pair of weights to get 3.7.', 'start': 898.955, 'duration': 5.886}, {'end': 910.506, 'text': "We do this twice because we started out with two position encoded values that represent the word let's.", 'start': 904.841, 'duration': 5.665}, {'end': 917.273, 'text': "And, after doing the math two times, we still have two values representing the word let's.", 'start': 911.565, 'duration': 5.708}, {'end': 919.676, 'text': "Josh, I don't get it.", 'start': 918.134, 'duration': 1.542}, {'end': 926.225, 'text': "If we want two values to represent, let's why don't we just use the two values we started with?", 'start': 920.558, 'duration': 5.667}, {'end': 930.497, 'text': "That's a great question, Squatch, and we'll answer it.", 'start': 926.994, 'duration': 3.503}, {'end': 931.258, 'text': 'in a little bit.', 'start': 930.497, 'duration': 0.761}, {'end': 943.069, 'text': 'Grrr. Anyway, for now, just know that we have these two new values to represent the word LETS and in Transformer terminology we call them query values.', 'start': 931.979, 'duration': 11.09}, {'end': 950.916, 'text': "And now that we have query values for the word LETS, let's use them to calculate the similarity between itself and the word GO.", 'start': 943.85, 'duration': 7.066}, {'end': 958.536, 'text': 'And we do this by creating two new values just like we did for the query to represent the word lets.', 'start': 952.253, 'duration': 6.283}, {'end': 962.838, 'text': 'And we create two new values to represent the word go.', 'start': 959.516, 'duration': 3.322}, {'end': 967.199, 'text': 'Both sets of new values are called key values.', 'start': 963.898, 'duration': 3.301}, {'end': 972.102, 'text': 'And we use them to calculate similarities with the query for lets.', 'start': 968.08, 'duration': 4.022}, {'end': 979.625, 'text': 'One way to calculate similarities between the query and the keys is to calculate something called a dot product.', 'start': 973.342, 'duration': 6.283}], 'summary': 'Transformers use self-attention to encode words by calculating similarities, as demonstrated with the example of lets and go.', 'duration': 91.065, 'max_score': 813.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY813776.jpg'}, {'end': 995.092, 'src': 'embed', 'start': 959.516, 'weight': 4, 'content': [{'end': 962.838, 'text': 'And we create two new values to represent the word go.', 'start': 959.516, 'duration': 3.322}, {'end': 967.199, 'text': 'Both sets of new values are called key values.', 'start': 963.898, 'duration': 3.301}, {'end': 972.102, 'text': 'And we use them to calculate similarities with the query for lets.', 'start': 968.08, 'duration': 4.022}, {'end': 979.625, 'text': 'One way to calculate similarities between the query and the keys is to calculate something called a dot product.', 'start': 973.342, 'duration': 6.283}, {'end': 985.866, 'text': 'For example, in order to calculate the dot product similarity between the query and key,', 'start': 980.523, 'duration': 5.343}, {'end': 995.092, 'text': 'for lets we simply multiply each pair of numbers together and then add the products to get 11.7..', 'start': 985.866, 'duration': 9.226}], 'summary': "New key values created to calculate similarities with query for 'lets', resulting in a dot product similarity of 11.7.", 'duration': 35.576, 'max_score': 959.516, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY959516.jpg'}], 'start': 766.288, 'title': 'Transformer mechanism and key values', 'summary': 'Discusses the self-attention mechanism in transformers, showcasing its role in correctly associating words in a sentence and the calculation of similarities, with a detailed example. it also explains the derivation of query and key values in a transformer, emphasizing the calculation of dot product similarity and their significance in determining word similarity.', 'chapters': [{'end': 879.067, 'start': 766.288, 'title': 'Transformer self-attention mechanism', 'summary': "Discusses how the self-attention mechanism in transformers helps in correctly associating words in a sentence, by calculating similarities and determining the impact on word encoding, with an example using the word 'it' referring to 'pizza' or 'oven'.", 'duration': 112.779, 'highlights': ["Self-attention mechanism in transformers helps in correctly associating words in a sentence. The self-attention mechanism ensures that the transformer correctly associates words in a sentence, such as 'it' with 'pizza' or 'oven'.", "Calculation of similarities between words in a sentence helps determine the impact on word encoding. The self-attention mechanism calculates the similarities between words in a sentence to determine the impact on word encoding, based on the frequency of association, such as 'it' being more commonly associated with 'pizza' than 'oven'.", "Example using the word 'it' referring to 'pizza' or 'oven'. The chapter provides an example of the word 'it' potentially referring to 'pizza' or 'oven', emphasizing the importance for the transformer to correctly associate the word with the intended meaning."]}, {'end': 1059.642, 'start': 879.768, 'title': 'Transformer query and key values', 'summary': 'Explains the process of deriving query and key values in a transformer, demonstrating the calculation of dot product similarity and the significance of these values in determining similarity between words.', 'duration': 179.874, 'highlights': ["The process of deriving query and key values involves multiplying positional encoded values by weights to obtain new values representing the words, which are used to calculate similarities using dot product, yielding specific similarity values like 11.7 for 'lets' relative to itself and -2.6 for 'lets' relative to the word 'go'.", "The significance of the similarity values is emphasized, indicating that 'lets' is much more similar to itself than to the word 'go', highlighting the importance of these values in determining word similarity in the transformer model.", "Explanation of the importance of similarity values is provided by relating it to an example where the word 'it' should have a relatively large similarity value with respect to 'pizza' and not 'oven', showcasing the relevance of similarity calculations in the given context."]}], 'duration': 293.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY766288.jpg', 'highlights': ['Self-attention mechanism ensures correct word association in a sentence.', 'Calculation of similarities impacts word encoding based on frequency.', 'Example emphasizes the importance of correctly associating words.', 'Deriving query and key values involves positional encoded values.', 'Significance of similarity values in determining word similarity.', 'Importance of similarity values illustrated with a specific example.']}, {'end': 1727.853, 'segs': [{'end': 1114.187, 'src': 'embed', 'start': 1084.232, 'weight': 2, 'content': [{'end': 1093.176, 'text': 'So we can think of the output of the softmax function as a way to determine what percentage of each input word we should use to encode the word lets.', 'start': 1084.232, 'duration': 8.944}, {'end': 1098.978, 'text': 'In this case, because lets is so much more similar to itself than the word go,', 'start': 1094.076, 'duration': 4.902}, {'end': 1104.2, 'text': "we'll use 100% of the word lets to encode lets and 0% of the word go to encode the word lets.", 'start': 1098.978, 'duration': 5.222}, {'end': 1114.187, 'text': "Note there's a lot more to be said about the softmax function so if you're interested check out the quest.", 'start': 1108.542, 'duration': 5.645}], 'summary': 'Softmax function determines word encoding percentages. lets: 100%, go: 0%', 'duration': 29.955, 'max_score': 1084.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1084232.jpg'}, {'end': 1153.669, 'src': 'heatmap', 'start': 1108.542, 'weight': 0.89, 'content': [{'end': 1114.187, 'text': "Note there's a lot more to be said about the softmax function so if you're interested check out the quest.", 'start': 1108.542, 'duration': 5.645}, {'end': 1126.617, 'text': "Anyway because we want 100% of the word lets to encode lets we create two more values that we'll cleverly call values to represent the word lets.", 'start': 1115.108, 'duration': 11.509}, {'end': 1132.079, 'text': "and scale the values that represent Let's by 1.0.", 'start': 1127.577, 'duration': 4.502}, {'end': 1139.583, 'text': 'Then we create two values to represent the word Go and scale those values by 0.0.', 'start': 1132.079, 'duration': 7.504}, {'end': 1142.124, 'text': 'Lastly, we add the scaled values together.', 'start': 1139.583, 'duration': 2.541}, {'end': 1151.288, 'text': "And these sums, which combine separate encodings for both input words Let's and Go, relative to their similarity to Let's,", 'start': 1143.004, 'duration': 8.284}, {'end': 1153.669, 'text': "are the self-attention values for Let's.", 'start': 1151.288, 'duration': 2.381}], 'summary': "Creating word encodings for 'let's' and 'go' based on self-attention values.", 'duration': 45.127, 'max_score': 1108.542, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1108542.jpg'}, {'end': 1242.682, 'src': 'embed', 'start': 1216.209, 'weight': 1, 'content': [{'end': 1223.273, 'text': 'Likewise, we reuse the sets of weights for calculating self-attention keys and values for each input word.', 'start': 1216.209, 'duration': 7.064}, {'end': 1233.132, 'text': 'This means that no matter how many words are input into the transformer, We just reuse the same sets of weights for self-attention, queries,', 'start': 1224.033, 'duration': 9.099}, {'end': 1234.434, 'text': 'keys and values.', 'start': 1233.132, 'duration': 1.302}, {'end': 1242.682, 'text': 'The other thing I want to point out is that we can calculate the queries, keys, and values for each word at the same time.', 'start': 1235.635, 'duration': 7.047}], 'summary': 'Reusing weights for self-attention across words, allowing simultaneous calculation of queries, keys, and values.', 'duration': 26.473, 'max_score': 1216.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1216209.jpg'}, {'end': 1370.611, 'src': 'heatmap', 'start': 1279.912, 'weight': 0.858, 'content': [{'end': 1288.179, 'text': 'First, the new self-attention values for each word contain input from all of the other words, and this helps give each word context.', 'start': 1279.912, 'duration': 8.267}, {'end': 1293.283, 'text': 'And this can help establish how each word in the input is related to the others.', 'start': 1288.919, 'duration': 4.364}, {'end': 1302.511, 'text': 'Also, if we think of this unit with its weights for calculating queries, keys and values as a self-attention cell,', 'start': 1294.264, 'duration': 8.247}, {'end': 1312.747, 'text': 'then in order to correctly establish how words are related in complicated sentences and paragraphs, we can create a stack of self-attention cells,', 'start': 1302.511, 'duration': 10.236}, {'end': 1321.534, 'text': 'each with its own sets of weights that we apply to the position-encoded values for each word to capture different relationships among the words.', 'start': 1312.747, 'duration': 8.787}, {'end': 1330.08, 'text': 'In the manuscript that first described transformers, they stacked eight self-attention cells, and they called this multi-head attention.', 'start': 1322.495, 'duration': 7.585}, {'end': 1333.022, 'text': 'Why eight instead of 12 or 16? I have no idea.', 'start': 1331.061, 'duration': 1.961}, {'end': 1346.407, 'text': "Okay, going back to our simple example with only one self-attention cell, there's one more thing we need to do to encode the input.", 'start': 1337.862, 'duration': 8.545}, {'end': 1352.73, 'text': 'We take the position-encoded values and add them to the self-attention values.', 'start': 1347.347, 'duration': 5.383}, {'end': 1360.074, 'text': 'These bypasses are called residual connections and they make it easier to train complex neural networks.', 'start': 1353.55, 'duration': 6.524}, {'end': 1370.611, 'text': 'by allowing the self-attention layer to establish relationships among the input words without having to also preserve the word embedding and position encoding information.', 'start': 1361.024, 'duration': 9.587}], 'summary': 'Self-attention cells capture word relationships in transformer models.', 'duration': 90.699, 'max_score': 1279.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1279912.jpg'}, {'end': 1439.806, 'src': 'embed', 'start': 1389.972, 'weight': 4, 'content': [{'end': 1393.073, 'text': 'self-attention and residual connections.', 'start': 1389.972, 'duration': 3.101}, {'end': 1402.895, 'text': 'These four features allow the transformer to encode words into numbers, encode the positions of the words,', 'start': 1393.973, 'duration': 8.922}, {'end': 1409.477, 'text': 'encode the relationships among the words and relatively easily and quickly train in parallel.', 'start': 1402.895, 'duration': 6.582}, {'end': 1417.144, 'text': "That said, there are lots of extra things we can add to a transformer and we'll talk about those at the end of this quest.", 'start': 1410.441, 'duration': 6.703}, {'end': 1425.907, 'text': "Bam! So, now that we've encoded the English input phrase, let's go, it's time to decode it into Spanish.", 'start': 1417.844, 'duration': 8.063}, {'end': 1430.889, 'text': 'In other words, the first part of a transformer is called an encoder.', 'start': 1426.888, 'duration': 4.001}, {'end': 1434.711, 'text': "And now it's time to create the second part, a decoder.", 'start': 1431.61, 'duration': 3.101}, {'end': 1439.806, 'text': 'The decoder, just like the encoder, starts with word embedding.', 'start': 1435.762, 'duration': 4.044}], 'summary': 'Transformer uses self-attention and residual connections to encode, position, and relate words, allowing parallel training. decoder follows similar process for translation.', 'duration': 49.834, 'max_score': 1389.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1389972.jpg'}, {'end': 1577.67, 'src': 'embed', 'start': 1552.055, 'weight': 0, 'content': [{'end': 1557.438, 'text': 'One key concept from earlier was that we created a single unit to process an input word.', 'start': 1552.055, 'duration': 5.383}, {'end': 1561.5, 'text': 'And then we just copied that unit for each word in the input.', 'start': 1558.318, 'duration': 3.182}, {'end': 1566.705, 'text': 'and if we had more words, we just make more copies of the same unit.', 'start': 1562.523, 'duration': 4.182}, {'end': 1572.248, 'text': 'By creating a single unit that can be copied for each input word,', 'start': 1567.625, 'duration': 4.623}, {'end': 1577.67, 'text': 'the transformer can do all of the computation for each word in the input at the same time.', 'start': 1572.248, 'duration': 5.422}], 'summary': 'Transformer can process all input words simultaneously by creating single unit for each word.', 'duration': 25.615, 'max_score': 1552.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1552055.jpg'}], 'start': 1059.642, 'title': 'Transformer parallel computation', 'summary': 'Discusses the concept of using a single unit to process an input word and then copy that unit for each word in the input, enabling the transformer to perform all computations for each word at the same time, leading to faster processing of a large number of words on computing cores like gpu.', 'chapters': [{'end': 1139.583, 'start': 1059.642, 'title': 'Softmax function in word encoding', 'summary': "Explains the use of softmax function to determine the percentage of each input word used to encode a specific word, with a specific example indicating 100% usage of the word 'lets' and 0% of the word 'go'.", 'duration': 79.941, 'highlights': ['The softmax function preserves the order of input values from low to high and translates them into numbers between 0 and 1 that add up to 1, determining the percentage of each input word used to encode a specific word.', "The example illustrates using 100% of the word 'lets' to encode itself and 0% of the word 'go' to encode 'lets', demonstrating the practical application of the softmax function in word encoding."]}, {'end': 1551.095, 'start': 1139.583, 'title': 'Transformer encoding process', 'summary': 'Explains the process of encoding input words using self-attention, reusing weights for queries, keys, and values, and leveraging parallel computing to establish relationships among the words, with a focus on the features and components of a simple transformer, leading to the preparation for decoding the input into spanish.', 'duration': 411.512, 'highlights': ['The process of encoding input words using self-attention, reusing weights for queries, keys, and values, and leveraging parallel computing to establish relationships among the words The chapter details the process of encoding input words using self-attention, reusing the same set of weights for calculating self-attention queries, keys, and values for each input word, and taking advantage of parallel computing for fast computation.', 'Features and components of a simple transformer, including word embedding, positional encoding, self-attention, and residual connections It explains the features and components of a simple transformer, such as word embedding, positional encoding, self-attention, and residual connections, which enable the transformer to encode words into numbers, encode word positions, establish word relationships, and train relatively easily and quickly in parallel.', 'Preparation for decoding the input into Spanish The chapter discusses the preparation for decoding the input into Spanish, introducing the concept of the encoder and the upcoming creation of the decoder, starting with word embedding for the output vocabulary.']}, {'end': 1727.853, 'start': 1552.055, 'title': "Transformer's parallel computation", 'summary': 'Discusses the concept of using a single unit to process an input word and then copy that unit for each word in the input, enabling the transformer to perform all computations for each word at the same time, leading to faster processing of a large number of words on computing cores like gpu.', 'duration': 175.798, 'highlights': ['The transformer uses a single unit to process an input word and then copies it for each word, enabling it to perform all computations for each word at the same time, leading to faster processing on computing cores like GPU.', 'By performing all computations at the same time rather than sequentially for each word, the transformer can process a lot of words relatively quickly on a chip with a lot of computing cores, like a GPU or multiple chips in the cloud.', 'The transformer keeps track of how words are related within a sentence using self-attention, and it also needs to keep track of the relationships between the input sentence and the output when translating.', "In the process of translating a sentence, it's important to keep track of the relationships between the input sentence and the output to ensure the accuracy of the translation and prevent opposite meanings.", 'The self-attention values for the EOS token are negative 2.8 and negative 2.3, and the sets of weights used to calculate the self-attention in the decoder are different from those used in the encoder.']}], 'duration': 668.211, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1059642.jpg', 'highlights': ['The transformer uses a single unit to process an input word and then copies it for each word, enabling it to perform all computations for each word at the same time, leading to faster processing on computing cores like GPU.', 'The process of encoding input words using self-attention, reusing weights for queries, keys, and values, and leveraging parallel computing to establish relationships among the words.', 'The softmax function preserves the order of input values from low to high and translates them into numbers between 0 and 1 that add up to 1, determining the percentage of each input word used to encode a specific word.', "The example illustrates using 100% of the word 'lets' to encode itself and 0% of the word 'go' to encode 'lets', demonstrating the practical application of the softmax function in word encoding.", 'Features and components of a simple transformer, including word embedding, positional encoding, self-attention, and residual connections.', 'Preparation for decoding the input into Spanish, introducing the concept of the encoder and the upcoming creation of the decoder, starting with word embedding for the output vocabulary.']}, {'end': 1921.68, 'segs': [{'end': 1755.58, 'src': 'embed', 'start': 1728.754, 'weight': 1, 'content': [{'end': 1733.855, 'text': "So it's super important for the decoder to keep track of the significant words in the input.", 'start': 1728.754, 'duration': 5.101}, {'end': 1743.197, 'text': 'So the main idea of encoder-decoder attention is to allow the decoder to keep track of the significant words in the input.', 'start': 1734.855, 'duration': 8.342}, {'end': 1750.058, 'text': 'Now that we know the main idea behind encoder-decoder attention, here are the details.', 'start': 1744.577, 'duration': 5.481}, {'end': 1755.58, 'text': "First, to give us a little more room, let's consolidate the math and the diagrams.", 'start': 1751.018, 'duration': 4.562}], 'summary': 'Encoder-decoder attention tracks significant words in input, consolidates math and diagrams.', 'duration': 26.826, 'max_score': 1728.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1728754.jpg'}, {'end': 1830.04, 'src': 'heatmap', 'start': 1756.461, 'weight': 0.708, 'content': [{'end': 1764.745, 'text': 'Now, just like we did for self-attention, we create two new values to represent the query for the EOS token in the decoder.', 'start': 1756.461, 'duration': 8.284}, {'end': 1768.686, 'text': 'Then we create keys for each word in the encoder.', 'start': 1765.525, 'duration': 3.161}, {'end': 1779.772, 'text': 'And we calculate the similarities between the EOS token in the decoder and each word in the encoder by calculating the dot products just like before.', 'start': 1769.567, 'duration': 10.205}, {'end': 1784.527, 'text': 'Then we run the similarities through a softmax function,', 'start': 1780.686, 'duration': 3.841}, {'end': 1795.33, 'text': 'and this tells us to use 100% of the first input word and 0% of the second when the decoder determines what should be the first translated word.', 'start': 1784.527, 'duration': 10.803}, {'end': 1803.872, 'text': 'Now that we know what percentage of each input word to use when determining what should be the first translated word,', 'start': 1796.35, 'duration': 7.522}, {'end': 1806.513, 'text': 'we calculate values for each input word.', 'start': 1803.872, 'duration': 2.641}, {'end': 1810.971, 'text': 'and then scale those values by the softmax percentages.', 'start': 1807.509, 'duration': 3.462}, {'end': 1817.534, 'text': 'And then add the pairs of scaled values together to get the encoder-decoder attention values.', 'start': 1811.911, 'duration': 5.623}, {'end': 1825.738, 'text': "Bam! Now, to make room for the next step, let's consolidate the encoder-decoder attention in our diagram.", 'start': 1818.314, 'duration': 7.424}, {'end': 1830.04, 'text': 'Note, the sets of weights that we use to calculate the queries,', 'start': 1826.598, 'duration': 3.442}], 'summary': 'Creating attention values for decoder and encoder based on softmax percentages.', 'duration': 73.579, 'max_score': 1756.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1756461.jpg'}, {'end': 1864.003, 'src': 'embed', 'start': 1837.523, 'weight': 2, 'content': [{'end': 1843.528, 'text': 'However, just like for self-attention, the sets of weights are copied and reused for each word.', 'start': 1837.523, 'duration': 6.005}, {'end': 1849.512, 'text': 'This allows the transformer to be flexible with the length of the inputs and outputs.', 'start': 1844.328, 'duration': 5.184}, {'end': 1859.24, 'text': 'And also, we can stack encoder-decoder attention just like we can stack self-attention to keep track of words and complicated phrases.', 'start': 1850.413, 'duration': 8.827}, {'end': 1864.003, 'text': 'Bam Now we add another set of residual connections.', 'start': 1859.9, 'duration': 4.103}], 'summary': 'Transformer allows flexibility with input/output length and stacking of attention mechanisms.', 'duration': 26.48, 'max_score': 1837.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1837523.jpg'}, {'end': 1921.68, 'src': 'heatmap', 'start': 1864.827, 'weight': 0.777, 'content': [{'end': 1871.856, 'text': 'that allow the encoder-decoder attention to focus on the relationships between the output words and the input words,', 'start': 1864.827, 'duration': 7.029}, {'end': 1877.643, 'text': 'without having to preserve the self-attention or word and position encoding that happened earlier.', 'start': 1871.856, 'duration': 5.787}, {'end': 1880.908, 'text': 'Then we consolidate the math in the diagram.', 'start': 1878.504, 'duration': 2.404}, {'end': 1895.454, 'text': 'Lastly, we need a way to take these two values that represent the EOS token in the decoder and select one of the four output tokens.', 'start': 1882.048, 'duration': 13.406}, {'end': 1902.457, 'text': 'So we run these two values through a fully connected layer that has one input for each value that represents the current token.', 'start': 1895.454, 'duration': 7.003}, {'end': 1905.138, 'text': 'So in this case, we have two inputs.', 'start': 1903.057, 'duration': 2.081}, {'end': 1912.075, 'text': 'and one output for each token in the output vocabulary, which in this case means four outputs.', 'start': 1906.092, 'duration': 5.983}, {'end': 1921.68, 'text': 'a fully connected layer is just a simple neural network with weights, numbers we multiply the inputs by and biases,', 'start': 1913.696, 'duration': 7.984}], 'summary': 'The model uses encoder-decoder attention and a fully connected layer with 2 inputs and 4 outputs.', 'duration': 56.853, 'max_score': 1864.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1864827.jpg'}, {'end': 1921.68, 'src': 'embed', 'start': 1895.454, 'weight': 0, 'content': [{'end': 1902.457, 'text': 'So we run these two values through a fully connected layer that has one input for each value that represents the current token.', 'start': 1895.454, 'duration': 7.003}, {'end': 1905.138, 'text': 'So in this case, we have two inputs.', 'start': 1903.057, 'duration': 2.081}, {'end': 1912.075, 'text': 'and one output for each token in the output vocabulary, which in this case means four outputs.', 'start': 1906.092, 'duration': 5.983}, {'end': 1921.68, 'text': 'a fully connected layer is just a simple neural network with weights, numbers we multiply the inputs by and biases,', 'start': 1913.696, 'duration': 7.984}], 'summary': 'A fully connected layer with 2 inputs and 4 outputs is used for token representation in a neural network.', 'duration': 26.226, 'max_score': 1895.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1895454.jpg'}], 'start': 1728.754, 'title': 'Transformer encoder-decoder attention', 'summary': 'Covers the calculation of encoder-decoder attention values, transformer flexibility with input/output length, stacking encoder-decoder attention, and output token selection process through a fully connected layer.', 'chapters': [{'end': 1810.971, 'start': 1728.754, 'title': 'Encoder-decoder attention', 'summary': 'Explains the main idea of encoder-decoder attention, which is to allow the decoder to keep track of significant words in the input and how it calculates the softmax percentages to determine the translation of the first word.', 'duration': 82.217, 'highlights': ['The main idea of encoder-decoder attention is to allow the decoder to keep track of significant words in the input.', 'The softmax function is used to calculate the percentages of each input word to determine the first translated word.', 'The decoder uses 100% of the first input word and 0% of the second when determining the first translated word.']}, {'end': 1921.68, 'start': 1811.911, 'title': 'Transformer encoder-decoder attention', 'summary': 'Explains how to calculate encoder-decoder attention values, the flexibility of the transformer with the length of inputs and outputs, stacking encoder-decoder attention, and the process of selecting output tokens through a fully connected layer.', 'duration': 109.769, 'highlights': ['The sets of weights used for encoder-decoder attention are different from those used for self-attention. Explains the distinction between the sets of weights used for encoder-decoder attention and self-attention.', "The transformer's flexibility with the length of inputs and outputs is achieved by copying and reusing sets of weights for each word. Describes how the transformer maintains flexibility with varying input and output lengths by reusing sets of weights for each word.", 'The process of selecting output tokens involves running values through a fully connected layer with one input for each value representing the current token and one output for each token in the output vocabulary. Details the process of selecting output tokens through a fully connected layer, specifying the inputs, outputs, and the nature of the layer.']}], 'duration': 192.926, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1728754.jpg', 'highlights': ['The process of selecting output tokens involves running values through a fully connected layer with one input for each value representing the current token and one output for each token in the output vocabulary.', 'The main idea of encoder-decoder attention is to allow the decoder to keep track of significant words in the input.', "The transformer's flexibility with the length of inputs and outputs is achieved by copying and reusing sets of weights for each word.", 'The sets of weights used for encoder-decoder attention are different from those used for self-attention.']}, {'end': 2174.208, 'segs': [{'end': 2054.589, 'src': 'embed', 'start': 2008.231, 'weight': 0, 'content': [{'end': 2013.312, 'text': "At long last we've shown how a transformer can encode a simple input phrase.", 'start': 2008.231, 'duration': 5.081}, {'end': 2019.233, 'text': "let's go and decode the encoding into the translated phrase VAMOS.", 'start': 2013.312, 'duration': 5.921}, {'end': 2026.445, 'text': 'In summary transformers use word embedding to convert words into numbers.', 'start': 2020.382, 'duration': 6.063}, {'end': 2030.167, 'text': 'positional encoding to keep track of word order.', 'start': 2026.445, 'duration': 3.722}, {'end': 2036.51, 'text': 'self-attention to keep track of word relationships within the input and output phrases.', 'start': 2030.167, 'duration': 6.343}, {'end': 2046.456, 'text': 'encoder-decoder attention to keep track of things between the input and output phrases to make sure that important words in the input are not lost in the translation.', 'start': 2036.51, 'duration': 9.946}, {'end': 2054.589, 'text': 'and residual connections to allow each subunit, like self-attention, to focus on solving just one part of the problem.', 'start': 2047.443, 'duration': 7.146}], 'summary': 'Transformer encodes input phrase, using word embedding, positional encoding, self-attention, and encoder-decoder attention.', 'duration': 46.358, 'max_score': 2008.231, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY2008231.jpg'}, {'end': 2109.655, 'src': 'embed', 'start': 2081.882, 'weight': 2, 'content': [{'end': 2089.206, 'text': 'For example, they normalized the values after positional encoding and after self-attention in both the encoder and the decoder.', 'start': 2081.882, 'duration': 7.324}, {'end': 2096.65, 'text': 'Also, when we calculated attention values, we used the dot product to calculate the similarities,', 'start': 2090.128, 'duration': 6.522}, {'end': 2099.413, 'text': 'but you can use whatever similarity function you want.', 'start': 2096.65, 'duration': 2.763}, {'end': 2102.953, 'text': 'In the original Transformer manuscript,', 'start': 2100.512, 'duration': 2.441}, {'end': 2109.655, 'text': 'they calculated the similarities with the dot product divided by the square root of the number of embedding values per token.', 'start': 2102.953, 'duration': 6.702}], 'summary': 'Transformer model uses dot product for attention calculation and normalizes values after positional encoding and self-attention.', 'duration': 27.773, 'max_score': 2081.882, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY2081882.jpg'}], 'start': 1921.68, 'title': 'Neural machine translation process', 'summary': 'Explores neural machine translation, covering decoding steps, specific weights and functions, word embedding, positional encoding, self-attention, encoder-decoder attention, and enhancements to transformers, improving performance on larger vocabularies and longer phrases.', 'chapters': [{'end': 2006.731, 'start': 1921.68, 'title': 'Neural machine translation process', 'summary': 'Discusses the process of neural machine translation, highlighting the steps involved in decoding and the use of specific weights and functions, resulting in the correct translation of the input word into spanish.', 'duration': 85.051, 'highlights': ['The decoder goes through multiple steps, including adding positional encoding, calculating self-attention values, and using residual connections and fully connected layers, resulting in the correct translation of the input word into Spanish.', "The process involves running the output values through a final softmax function to select the first output word, Vamos, which is the Spanish translation for 'let's go'.", 'The second output from the decoder is EOS, indicating the completion of the decoding process.']}, {'end': 2054.589, 'start': 2008.231, 'title': 'Transformer basics: encoding and decoding', 'summary': 'Demonstrates how transformers use word embedding, positional encoding, self-attention, encoder-decoder attention, and residual connections to effectively encode and decode input phrases, ensuring accurate translation and word relationship tracking.', 'duration': 46.358, 'highlights': ['Transformers use word embedding, positional encoding, self-attention, encoder-decoder attention, and residual connections to encode and decode input phrases, ensuring accurate translation and word relationship tracking.', 'Self-attention is used to keep track of word relationships within the input and output phrases, ensuring accurate translation and word relationship tracking.', 'Encoder-decoder attention is utilized to ensure that important words in the input are not lost in the translation, ensuring accurate translation and word relationship tracking.']}, {'end': 2174.208, 'start': 2055.61, 'title': 'Enhancements to transformers', 'summary': 'Discusses enhancements to transformers, including normalization of values, calculation of attention values using dot product, and addition of neural networks with hidden layers to both the encoder and decoder, to improve performance on larger vocabularies and longer input and output phrases.', 'duration': 118.598, 'highlights': ['They had to normalize the values after every step, including after positional encoding and self-attention in both the encoder and the decoder.', 'In the original Transformer manuscript, they calculated the similarities with the dot product divided by the square root of the number of embedding values per token.', 'To give a transformer more weights and biases to fit to complicated data, additional neural networks with hidden layers can be added to both the encoder and decoder.']}], 'duration': 252.528, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zxQyTK8quyY/pics/zxQyTK8quyY1921680.jpg', 'highlights': ['Transformers use word embedding, positional encoding, self-attention, encoder-decoder attention, and residual connections to encode and decode input phrases, ensuring accurate translation and word relationship tracking.', 'The decoder goes through multiple steps, including adding positional encoding, calculating self-attention values, and using residual connections and fully connected layers, resulting in the correct translation of the input word into Spanish.', 'They had to normalize the values after every step, including after positional encoding and self-attention in both the encoder and the decoder.']}], 'highlights': ['The transformer uses a single unit to process an input word and then copies it for each word, enabling it to perform all computations for each word at the same time, leading to faster processing on computing cores like GPU.', 'Transformers use word embedding, positional encoding, self-attention, encoder-decoder attention, and residual connections to encode and decode input phrases, ensuring accurate translation and word relationship tracking.', "The chapter introduces positional encoding as a technique employed by transformers to maintain word order and demonstrates its application to the phrase 'Squatch Eats Pizza'.", 'The process of selecting output tokens involves running values through a fully connected layer with one input for each value representing the current token and one output for each token in the output vocabulary.', "The example illustrates using 100% of the word 'lets' to encode itself and 0% of the word 'go' to encode 'lets', demonstrating the practical application of the softmax function in word encoding.", 'The main idea of encoder-decoder attention is to allow the decoder to keep track of significant words in the input.', 'The chapter focuses on how a transformer neural network can translate a simple English sentence into Spanish, using word embedding and tokens.', 'The process of encoding input words using self-attention, reusing weights for queries, keys, and values, and leveraging parallel computing to establish relationships among the words.', "The example of 'Squatch eats pizza' and 'pizza eats Squatch' demonstrates the significance of word order in conveying different meanings through the same words.", 'Back propagation optimizes weights in word embedding network.']}