title
Attention for Neural Networks, Clearly Explained!!!

description
Attention is one of the most important concepts behind Transformers and Large Language Models, like ChatGPT. However, it's not that complicated. In this StatQuest, we add Attention to a basic Sequence-to-Sequence (Seq2Seq or Encoder-Decoder) model and walk through how it works and is calculated, one step at a time. BAM!!! NOTE: This StatQuest is based on two manuscripts. 1) The manuscript that originally introduced Attention to Encoder-Decoder Models: Neural Machine Translation by Jointly Learning to Align and Translate: https://arxiv.org/abs/1409.0473 and 2) The manuscript that first used the Dot-Product similarity for Attention in a similar context: Effective Approaches to Attention-based Neural Machine Translation https://arxiv.org/abs/1508.04025 NOTE: This StatQuest assumes that you are already familiar with basic Encoder-Decoder neural networks. If not, check out the 'Quest: https://youtu.be/L8HKweZIOmg 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! https://www.paypal.me/statquest 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 3:14 The Main Idea of Attention 5:34 A worked out example of Attention 10:18 The Dot Product Similarity 11:52 Using similarity scores to calculate Attention values 13:27 Using Attention values to predict an output word 14:22 Summary of Attention #StatQuest #neuralnetwork #attention

detail
{'title': 'Attention for Neural Networks, Clearly Explained!!!', 'heatmap': [{'end': 848.018, 'start': 778.437, 'weight': 0.704}], 'summary': 'Explains attention in encoder-decoder models, introduces lstm units for long-term memory, and discusses the role of attention in understanding transformers for large language models like chatgpt.', 'chapters': [{'end': 194.441, 'segs': [{'end': 71.61, 'src': 'embed', 'start': 37.128, 'weight': 2, 'content': [{'end': 38.009, 'text': 'C, curious.', 'start': 37.128, 'duration': 0.881}, {'end': 39.791, 'text': 'Always be curious.', 'start': 38.429, 'duration': 1.362}, {'end': 48.14, 'text': 'Note, this StatQuest assumes that you are already familiar with basic seek-to-seek and encoder-decoder neural networks.', 'start': 40.712, 'duration': 7.428}, {'end': 50.022, 'text': 'If not, check out the Quest.', 'start': 48.521, 'duration': 1.501}, {'end': 58.325, 'text': 'I also want to give a special triple-bam shout-out to Lena Vojta and her GitHub Tutorials NLP course for you,', 'start': 50.962, 'duration': 7.363}, {'end': 60.466, 'text': 'because it helped me a lot with this stat quest.', 'start': 58.325, 'duration': 2.141}, {'end': 71.61, 'text': "Hey look, it's Statsquatch and the Normalsaurus! Hey Josh, last time we used a basic encoder-decoder model to translate Let's Go into Spanish.", 'start': 61.226, 'duration': 10.384}], 'summary': "Statquest assumes familiarity with basic neural networks and gives a shout-out to lena vojta's nlp course.", 'duration': 34.482, 'max_score': 37.128, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k37128.jpg'}, {'end': 194.441, 'src': 'embed', 'start': 87.854, 'weight': 0, 'content': [{'end': 92.319, 'text': "Wah-wah I'm sorry you're having trouble with translating, Squatch.", 'start': 87.854, 'duration': 4.465}, {'end': 104.988, 'text': 'The problem is that in the encoder in a basic encoder-decoder, unrolling the LSTMs compresses the entire input sentence into a single context vector.', 'start': 93.1, 'duration': 11.888}, {'end': 113.735, 'text': "This works fine for short phrases like let's go, but if we had a bigger input vocabulary with thousands of words,", 'start': 105.749, 'duration': 7.986}, {'end': 119.147, 'text': 'then we could input longer and more complicated sentences like this.', 'start': 114.704, 'duration': 4.443}, {'end': 125.252, 'text': "Don't eat the delicious looking and smelling pizza.", 'start': 119.808, 'duration': 5.444}, {'end': 132.458, 'text': 'But for longer phrases, even with LSTMs, words that are input early on can be forgotten.', 'start': 126.153, 'duration': 6.305}, {'end': 145.019, 'text': "and in this case, if we forget the first word, don't, then don't eat the delicious, looking and smelling pizza turns into eat the delicious,", 'start': 133.733, 'duration': 11.286}, {'end': 155.905, 'text': "looking and smelling pizza, and these two sentences have completely opposite meanings, so sometimes it's super important to remember the first word.", 'start': 145.019, 'duration': 10.886}, {'end': 158.409, 'text': 'So what are we going to do??', 'start': 157.068, 'duration': 1.341}, {'end': 161.492, 'text': 'Well, you might remember that basic,', 'start': 159.29, 'duration': 2.202}, {'end': 169.498, 'text': 'recurrent neural networks had problems with long-term memories because they ran both the long and short-term memories through a single path.', 'start': 161.492, 'duration': 8.006}, {'end': 179.367, 'text': 'And that the main idea of long short-term memory units is that they solve this problem by providing separate paths for long and short-term memories.', 'start': 170.399, 'duration': 8.968}, {'end': 187.795, 'text': 'Well, even with separate paths, if we have a lot of data, both paths have to carry a lot of information.', 'start': 180.529, 'duration': 7.266}, {'end': 194.441, 'text': "And that means that a word at the start of a long phrase, like don't, can still get lost.", 'start': 188.776, 'duration': 5.665}], 'summary': 'Challenges in encoding long sentences with lstms and addressing long-term memory issues in neural networks.', 'duration': 106.587, 'max_score': 87.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k87854.jpg'}], 'start': 0.766, 'title': 'Understanding attention in encoder-decoder model and lstm long short-term memory units', 'summary': 'Covers attention in encoder-decoder models, addressing limitations in handling larger vocabularies, and introduces lstm units for retaining long-term memories in recurrent neural networks. it also emphasizes the importance of basic seek-to-seek and encoder-decoder neural networks.', 'chapters': [{'end': 113.735, 'start': 0.766, 'title': 'Understanding attention in encoder-decoder model', 'summary': 'Explains the concept of attention in encoder-decoder models, highlighting the limitation of basic models in handling larger input vocabularies and expressing gratitude to contributors. it emphasizes the importance of being familiar with basic seek-to-seek and encoder-decoder neural networks and recommends additional resources for learning.', 'duration': 112.969, 'highlights': ['The problem with basic encoder-decoder models is that unrolling the LSTMs compresses the entire input sentence into a single context vector, limiting its effectiveness for larger input vocabularies.', 'The transcript expresses gratitude to Lena Vojta and her GitHub Tutorials NLP course for their valuable contribution to the stat quest.', 'The chapter emphasizes the importance of being familiar with basic seek-to-seek and encoder-decoder neural networks before delving into the concept of attention in the models.']}, {'end': 194.441, 'start': 114.704, 'title': 'Lstm long short-term memory units', 'summary': 'Explains the importance of remembering the first word in a sentence when using long short-term memory (lstm) units, and discusses the challenges of retaining long-term memories in recurrent neural networks. it also introduces the concept of separate paths for long and short-term memories in lstm units.', 'duration': 79.737, 'highlights': ['LSTM units aim to solve the problem of long-term memory retention by providing separate paths for long and short-term memories. Introduces the concept of separate paths in LSTM units.', 'Explains the importance of remembering the first word in a sentence when using LSTM units, as forgetting it can completely change the meaning of the sentence. Emphasizes the significance of remembering the first word in a sentence when using LSTM units.', 'Discusses the challenges of retaining long-term memories in recurrent neural networks, as words input early on can be forgotten, leading to a change in sentence meaning. Highlights the challenges of retaining long-term memories in recurrent neural networks.']}], 'duration': 193.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k766.jpg', 'highlights': ['LSTM units provide separate paths for long and short-term memories.', 'Unrolling LSTMs compresses input sentence into a single context vector, limiting effectiveness for larger vocabularies.', 'Importance of being familiar with basic seek-to-seek and encoder-decoder neural networks.', 'Challenges of retaining long-term memories in recurrent neural networks.', 'Significance of remembering the first word in a sentence when using LSTM units.']}, {'end': 678.675, 'segs': [{'end': 265.193, 'src': 'embed', 'start': 195.342, 'weight': 0, 'content': [{'end': 203.729, 'text': 'So the main idea of attention is to add a bunch of new paths from the encoder to the decoder, one per input value,', 'start': 195.342, 'duration': 8.387}, {'end': 208.332, 'text': 'so that each step of the decoder can directly access input values.', 'start': 203.729, 'duration': 4.603}, {'end': 218.349, 'text': "The basic encoder-decoder plus attention models that we're going to talk about today are totally awesome,", 'start': 211.637, 'duration': 6.712}, {'end': 224.359, 'text': "but they are also a stepping stone to learning about Transformers, which we'll talk about in future Stat Quests.", 'start': 218.349, 'duration': 6.01}, {'end': 232.924, 'text': "In other words, today we're taking another step in our quest to understand transformers which form the basis of big, fancy,", 'start': 225.379, 'duration': 7.545}, {'end': 235.806, 'text': 'large language models like ChatGPT.', 'start': 232.924, 'duration': 2.882}, {'end': 241.95, 'text': 'Now, if you remember, from the StatQuest on encoder-decoder models.', 'start': 236.806, 'duration': 5.144}, {'end': 250.095, 'text': 'an encoder-decoder model can be as simple as an embedding layer attached to a single long short-term memory unit.', 'start': 241.95, 'duration': 8.145}, {'end': 256.367, 'text': 'but if we want a slightly more fancy encoder, we can add additional LSTM cells.', 'start': 251.104, 'duration': 5.263}, {'end': 265.193, 'text': "Now we'll initialize the long and short term memories, the cell and hidden states in the LSTMs in the encoder with zeros.", 'start': 257.468, 'duration': 7.725}], 'summary': 'Using attention to create paths from encoder to decoder for direct access to input values. also, a step towards understanding transformers for future stat quests.', 'duration': 69.851, 'max_score': 195.342, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k195342.jpg'}, {'end': 454.033, 'src': 'embed', 'start': 428.524, 'weight': 4, 'content': [{'end': 439.271, 'text': 'And we also want to calculate a similarity score between the LSTM outputs from the second step in the encoder and the LSTM outputs from the first step in the decoder.', 'start': 428.524, 'duration': 10.747}, {'end': 448.744, 'text': 'There are a lot of ways to calculate the similarity of words, or more precisely, sequences of numbers that represent words.', 'start': 440.27, 'duration': 8.474}, {'end': 454.033, 'text': 'And different attention algorithms use different ways to compare these sequences.', 'start': 449.746, 'duration': 4.287}], 'summary': 'Calculating similarity score between lstm outputs for encoder and decoder.', 'duration': 25.509, 'max_score': 428.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k428524.jpg'}, {'end': 685.621, 'src': 'embed', 'start': 657.757, 'weight': 5, 'content': [{'end': 666.444, 'text': "It's super easy to calculate and, roughly speaking, large positive numbers mean things are more similar than small positive numbers.", 'start': 657.757, 'duration': 8.687}, {'end': 672.87, 'text': 'And large negative numbers mean things are more completely backwards than small negative numbers.', 'start': 667.445, 'duration': 5.425}, {'end': 678.675, 'text': "The other nice thing about the dot product is that it's easy to add to our diagram.", 'start': 674.051, 'duration': 4.624}, {'end': 685.621, 'text': 'We simply multiply each pair of output values together and then add them all together.', 'start': 679.576, 'duration': 6.045}], 'summary': 'Dot product simplifies similarity calculation and diagram addition.', 'duration': 27.864, 'max_score': 657.757, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k657757.jpg'}], 'start': 195.342, 'title': 'Encoder-decoder models & attention', 'summary': 'Discusses the concept of attention in encoder-decoder models, emphasizing the addition of new paths from the encoder to the decoder and its role in understanding transformers for large language models like chatgpt. it also introduces the concept of an encoder-decoder model with attention, explaining the process of initializing memories, creating a context vector, adding attention, and calculating similarity scores.', 'chapters': [{'end': 241.95, 'start': 195.342, 'title': 'Encoder-decoder models & attention', 'summary': 'Discusses the concept of attention in encoder-decoder models, emphasizing the addition of new paths from the encoder to the decoder, and its role as a stepping stone to understanding transformers for large language models like chatgpt.', 'duration': 46.608, 'highlights': ['The addition of new paths from the encoder to the decoder in the attention model allows each step of the decoder to directly access input values.', 'The concept of attention serves as a stepping stone in understanding Transformers, which are fundamental for large language models like ChatGPT.', 'Attention models are a significant component of encoder-decoder models and pave the way for learning about advanced models like Transformers.']}, {'end': 678.675, 'start': 241.95, 'title': 'Encoder-decoder with attention', 'summary': 'Introduces the concept of an encoder-decoder model with attention, explaining the process of initializing the long and short-term memories, creating a context vector, adding attention to the model, and calculating similarity scores using cosine similarity and dot product.', 'duration': 436.725, 'highlights': ['The chapter explains the process of initializing the long and short term memories, creating a context vector, and adding attention to the model. The process of initializing the long and short-term memories, creating a context vector, and adding attention to the model is explained.', 'The chapter describes the calculation of similarity scores using cosine similarity and dot product, providing an example of calculating the cosine similarity between the output values from the encoder and decoder LSTM cells. The calculation of similarity scores using cosine similarity and dot product is described, including an example of calculating the cosine similarity between the output values from the encoder and decoder LSTM cells.', 'The chapter emphasizes the ease and practicality of using the dot product for calculating similarity, highlighting its simplicity and its compatibility with the comparison of similarity scores for different LSTM cells. The ease and practicality of using the dot product for calculating similarity, along with its compatibility with the comparison of similarity scores for different LSTM cells, is emphasized.']}], 'duration': 483.333, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k195342.jpg', 'highlights': ['The addition of new paths from the encoder to the decoder in the attention model allows each step of the decoder to directly access input values.', 'The concept of attention serves as a stepping stone in understanding Transformers, which are fundamental for large language models like ChatGPT.', 'Attention models are a significant component of encoder-decoder models and pave the way for learning about advanced models like Transformers.', 'The chapter explains the process of initializing the long and short term memories, creating a context vector, and adding attention to the model.', 'The chapter describes the calculation of similarity scores using cosine similarity and dot product, providing an example of calculating the cosine similarity between the output values from the encoder and decoder LSTM cells.', 'The ease and practicality of using the dot product for calculating similarity, along with its compatibility with the comparison of similarity scores for different LSTM cells, is emphasized.']}, {'end': 949.899, 'segs': [{'end': 740.944, 'src': 'embed', 'start': 679.576, 'weight': 3, 'content': [{'end': 685.621, 'text': 'We simply multiply each pair of output values together and then add them all together.', 'start': 679.576, 'duration': 6.045}, {'end': 690.073, 'text': 'And we get negative 0.41.', 'start': 686.69, 'duration': 3.383}, {'end': 698.359, 'text': 'Likewise, we can compute a similarity score with the dot product between the second input word, go, and the EOS token.', 'start': 690.073, 'duration': 8.286}, {'end': 700.4, 'text': 'And we get 0.01.', 'start': 699.299, 'duration': 1.101}, {'end': 710.928, 'text': "Now we've got similarity scores for both input words, let's and go, relative to the EOS token in the decoder.", 'start': 700.4, 'duration': 10.528}, {'end': 717.646, 'text': "Bam!. Hey, Josh, it's great that we have scores, but how do we use them?", 'start': 711.689, 'duration': 5.957}, {'end': 725.792, 'text': 'Well, we can see that the similarity score between GO and the EOS token 0.01,', 'start': 718.627, 'duration': 7.165}, {'end': 732.297, 'text': 'is higher than the score between LETS and the EOS token negative 0.41..', 'start': 725.792, 'duration': 6.505}, {'end': 740.944, 'text': 'And since the score for GO is higher, we want the encoding for GO to have more influence on the first word that comes out of the decoder.', 'start': 732.297, 'duration': 8.647}], 'summary': 'Compute similarity scores for input words and use them to influence decoder output.', 'duration': 61.368, 'max_score': 679.576, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k679576.jpg'}, {'end': 848.018, 'src': 'heatmap', 'start': 778.437, 'weight': 0.704, 'content': [{'end': 784.242, 'text': 'So we scale the values for the first encoded word, lets, by 0.4.', 'start': 778.437, 'duration': 5.805}, {'end': 790.553, 'text': 'And we scale the values for the second encoded word, go, by 0.6.', 'start': 784.242, 'duration': 6.311}, {'end': 793.776, 'text': 'And lastly, we add the scaled values together.', 'start': 790.553, 'duration': 3.223}, {'end': 803.685, 'text': 'These sums, which combine the separate encodings for both input words, lets and go, relative to their similarity to EOS,', 'start': 794.637, 'duration': 9.048}, {'end': 806.007, 'text': 'are the attention values for EOS.', 'start': 803.685, 'duration': 2.322}, {'end': 815.895, 'text': 'Bam! Now, all we need to do to determine the first output word is plug the attention values into a fully connected layer.', 'start': 806.747, 'duration': 9.148}, {'end': 821.576, 'text': 'and plug the encodings for EOS into the same fully connected layer.', 'start': 816.795, 'duration': 4.781}, {'end': 830.318, 'text': 'And do the math… and run the output values through a softmax function to select the first output word, Vamos.', 'start': 822.356, 'duration': 7.962}, {'end': 841.661, 'text': 'Bam! Now, because the output was not the EOS token, we need to unroll the embedding layer and the LSTMs in the decoder.', 'start': 831.258, 'duration': 10.403}, {'end': 848.018, 'text': "and plug the translated word, Vamos, into the decoder's unrolled embedding layer.", 'start': 842.594, 'duration': 5.424}], 'summary': 'Scaled and summed values to determine attention values for eos and select first output word vamos.', 'duration': 69.581, 'max_score': 778.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k778437.jpg'}, {'end': 821.576, 'src': 'embed', 'start': 794.637, 'weight': 2, 'content': [{'end': 803.685, 'text': 'These sums, which combine the separate encodings for both input words, lets and go, relative to their similarity to EOS,', 'start': 794.637, 'duration': 9.048}, {'end': 806.007, 'text': 'are the attention values for EOS.', 'start': 803.685, 'duration': 2.322}, {'end': 815.895, 'text': 'Bam! Now, all we need to do to determine the first output word is plug the attention values into a fully connected layer.', 'start': 806.747, 'duration': 9.148}, {'end': 821.576, 'text': 'and plug the encodings for EOS into the same fully connected layer.', 'start': 816.795, 'duration': 4.781}], 'summary': 'Combining encodings of input words lets and go to determine first output word.', 'duration': 26.939, 'max_score': 794.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k794637.jpg'}, {'end': 904.864, 'src': 'embed', 'start': 855.222, 'weight': 0, 'content': [{'end': 860.185, 'text': "And the second output from the decoder is EOS, so we're done decoding.", 'start': 855.222, 'duration': 4.963}, {'end': 871.433, 'text': 'Triple bam! In summary, when we add attention to a basic encoder-decoder model, the encoder pretty much stays the same.', 'start': 861.106, 'duration': 10.327}, {'end': 878.834, 'text': 'But now, each step of decoding has access to the individual encodings for each input word.', 'start': 872.472, 'duration': 6.362}, {'end': 890.458, 'text': 'And we use similarity scores and the softmax function to determine what percentage of each encoded input word should be used to help predict the next output word.', 'start': 879.954, 'duration': 10.504}, {'end': 897.54, 'text': 'Now that we have attention added to the model, you might wonder if we still need the LSTMs.', 'start': 892.198, 'duration': 5.342}, {'end': 901.122, 'text': "Well, it turns out we don't need them.", 'start': 898.601, 'duration': 2.521}, {'end': 904.864, 'text': "And we'll talk more about that when we learn about transformers.", 'start': 901.542, 'duration': 3.322}], 'summary': 'Adding attention to encoder-decoder model improves decoding process, eliminating need for lstms.', 'duration': 49.642, 'max_score': 855.222, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k855222.jpg'}], 'start': 679.576, 'title': 'Enhancing encoder-decoder model', 'summary': 'Delves into computing similarity scores and adding attention to an encoder-decoder model, achieving improved decoding performance with the use of dot product, softmax function, and percentage determination.', 'chapters': [{'end': 740.944, 'start': 679.576, 'title': 'Computing similarity scores', 'summary': 'Discusses computing similarity scores using dot product and illustrates the use of scores to influence the output of the decoder.', 'duration': 61.368, 'highlights': ['The similarity score between GO and the EOS token is 0.01, higher than the score between LETS and the EOS token negative 0.41, influencing the encoding for GO to have more influence on the first word that comes out of the decoder.', 'The dot product between the second input word, go, and the EOS token results in a similarity score of 0.01.']}, {'end': 949.899, 'start': 741.857, 'title': 'Adding attention to encoder-decoder model', 'summary': 'Explains how attention is added to a basic encoder-decoder model, using similarity scores and softmax function to determine the percentage of each encoded input word used to predict the next output word, resulting in improved decoding performance.', 'duration': 208.042, 'highlights': ['The attention values for EOS are determined by scaling the separate encodings for input words, lets and go, and then adding the scaled values together. This process involves using 40% of the first encoded word, lets, and 60% of the second encoded word, go, to determine the first translated word, resulting in improved decoding performance.', 'The addition of attention to the model allows each step of decoding to have access to the individual encodings for each input word, enhancing the predictive capability of the model. This addition of attention to the model provides each step of decoding access to individual encodings for each input word, resulting in improved predictive capability.', 'The chapter mentions the optional use of LSTMs after adding attention to the model, indicating that they are no longer necessary and hinting at their discussion in learning about transformers. After adding attention to the model, the chapter suggests that LSTMs are no longer required and foreshadows their discussion in the context of learning about transformers.']}], 'duration': 270.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/PSs6nxngL6k/pics/PSs6nxngL6k679576.jpg', 'highlights': ['The addition of attention to the model allows each step of decoding to have access to the individual encodings for each input word, enhancing the predictive capability of the model.', 'The chapter mentions the optional use of LSTMs after adding attention to the model, indicating that they are no longer necessary and hinting at their discussion in learning about transformers.', 'The attention values for EOS are determined by scaling the separate encodings for input words, lets and go, and then adding the scaled values together. This process involves using 40% of the first encoded word, lets, and 60% of the second encoded word, go, to determine the first translated word, resulting in improved decoding performance.', 'The dot product between the second input word, go, and the EOS token results in a similarity score of 0.01.', 'The similarity score between GO and the EOS token is 0.01, higher than the score between LETS and the EOS token negative 0.41, influencing the encoding for GO to have more influence on the first word that comes out of the decoder.']}], 'highlights': ['The addition of new paths from the encoder to the decoder in the attention model allows each step of the decoder to directly access input values.', 'The concept of attention serves as a stepping stone in understanding Transformers, which are fundamental for large language models like ChatGPT.', 'The addition of attention to the model allows each step of decoding to have access to the individual encodings for each input word, enhancing the predictive capability of the model.', 'The attention values for EOS are determined by scaling the separate encodings for input words, lets and go, and then adding the scaled values together. This process involves using 40% of the first encoded word, lets, and 60% of the second encoded word, go, to determine the first translated word, resulting in improved decoding performance.', 'The chapter describes the calculation of similarity scores using cosine similarity and dot product, providing an example of calculating the cosine similarity between the output values from the encoder and decoder LSTM cells.', 'The ease and practicality of using the dot product for calculating similarity, along with its compatibility with the comparison of similarity scores for different LSTM cells, is emphasized.', 'LSTM units provide separate paths for long and short-term memories.', 'The chapter explains the process of initializing the long and short term memories, creating a context vector, and adding attention to the model.', 'Importance of being familiar with basic seek-to-seek and encoder-decoder neural networks.', 'Challenges of retaining long-term memories in recurrent neural networks.', 'Significance of remembering the first word in a sentence when using LSTM units.', 'The chapter mentions the optional use of LSTMs after adding attention to the model, indicating that they are no longer necessary and hinting at their discussion in learning about transformers.', 'The dot product between the second input word, go, and the EOS token results in a similarity score of 0.01.', 'The similarity score between GO and the EOS token is 0.01, higher than the score between LETS and the EOS token negative 0.41, influencing the encoding for GO to have more influence on the first word that comes out of the decoder.']}