1.11 GB | 00:29:18 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
Appendix A  Automatic differentiation made easy  (9.8 MB)
Appendix A  A typical training loop  (16.76 MB)
Appendix A  Exercise answers  (2.99 MB)
Appendix A  Further reading  (4.51 MB)
Appendix A  Implementing multilayer neural networks  (19.08 MB)
Appendix A  Introduction to PyTorch  (33.01 MB)
Appendix A  Optimizing training performance with GPUs  (38.68 MB)
Appendix A  Saving and loading models  (3.63 MB)
Appendix A  Seeing models as computation graphs  (5.71 MB)
Appendix A  Setting up efficient data loaders  (19.08 MB)
Appendix A  Summary  (4.48 MB)
Appendix A  Understanding tensors  (14 MB)
Appendix D  Adding Bells and Whistles to the Training Loop  (5.74 MB)
Appendix D  Cosine decay  (3.99 MB)
Appendix D  Gradient clipping  (5.83 MB)
Appendix D  The modified training function  (3.57 MB)
Appendix E  Initializing the model  (2.82 MB)
Appendix E  Parameter-efficient finetuning with LoRA (1)  (20.14 MB)
Appendix E  Parameter-efficient Finetuning with LoRA  (10.13 MB)
Appendix E  Preparing the dataset  (3.09 MB)
Chapter 1  Applications of LLMs  (4.94 MB)
Chapter 1  A closer look at the GPT architecture  (13.74 MB)
Chapter 1  Building a large language model  (3.99 MB)
Chapter 1  Introducing the transformer architecture  (20.58 MB)
Chapter 1  Stages of building and using LLMs  (10.97 MB)
Chapter 1  Summary  (3.61 MB)
Chapter 1  Understanding Large Language Models  (21.23 MB)
Chapter 1  Utilizing large datasets  (8.86 MB)
Chapter 2  Adding special context tokens  (14.78 MB)
Chapter 2  Byte pair encoding  (9.53 MB)
Chapter 2  Converting tokens into token IDs  (12.67 MB)
Chapter 2  Creating token embeddings  (11.93 MB)
Chapter 2  Data sampling with a sliding window  (21.35 MB)
Chapter 2  Encoding word positions  (14.05 MB)
Chapter 2  Summary  (6.54 MB)
Chapter 2  Tokenizing text  (11.56 MB)
Chapter 2  Working with Text Data  (21.67 MB)
Chapter 3  Attending to different parts of the input with self-attention  (34.74 MB)
Chapter 3  Capturing data dependencies with attention mechanisms  (7.02 MB)
Chapter 3  Coding Attention Mechanisms  (15.89 MB)
Chapter 3  Extending single-head attention to multi-head attention  (32.23 MB)
Chapter 3  Hiding future words with causal attention  (27.14 MB)
Chapter 3  Implementing self-attention with trainable weights  (38.89 MB)
Chapter 3  Summary  (6 MB)
Chapter 4  Adding shortcut connections  (14.61 MB)
Chapter 4  Coding the GPT model  (20.64 MB)
Chapter 4  Connecting attention and linear layers in a transformer block  (13.91 MB)
Chapter 4  Generating text  (19.76 MB)
Chapter 4  Implementing a feed forward network with GELU activations  (17.29 MB)
Chapter 4  Implementing a GPT model from Scratch To Generate Text  (30.36 MB)
Chapter 4  Normalizing activations with layer normalization  (29.93 MB)
Chapter 4  Summary  (5.61 MB)
Chapter 5  Decoding strategies to control randomness  (26.08 MB)
Chapter 5  Loading and saving model weights in PyTorch  (7.18 MB)
Chapter 5  Loading pretrained weights from OpenAI  (18.73 MB)
Chapter 5  Pretraining on Unlabeled Data  (63.37 MB)
Chapter 5  Summary  (4.29 MB)
Chapter 5   Training an LLM  (18.33 MB)
Chapter 6  Adding a classification head  (21.65 MB)
Chapter 6  Calculating the classification loss and accuracy  (12.04 MB)
Chapter 6  Creating data loaders  (14.7 MB)
Chapter 6  Finetuning for Classification  (11.72 MB)
Chapter 6  Finetuning the model on supervised data  (15.78 MB)
Chapter 6  Initializing a model with pretrained weights  (5.08 MB)
Chapter 6  Preparing the dataset  (9.65 MB)
Chapter 6  Summary  (5.37 MB)
Chapter 6  Using the LLM as a spam classifier  (4.13 MB)
Chapter 7  Conclusions  (8.12 MB)
Chapter 7  Creating data loaders for an instruction dataset  (9.42 MB)
Chapter 7  Evaluating the finetuned LLM  (23.67 MB)
Chapter 7  Extracting and saving responses  (14.25 MB)
Chapter 7  Finetuning the LLM on instruction data  (17.46 MB)
Chapter 7  Finetuning to Follow Instructions  (8.6 MB)
Chapter 7  Loading a pretrained LLM  (11.09 MB)
Chapter 7  Organizing data into training batches  (33.77 MB)
Chapter 7  Preparing a dataset for supervised instruction finetuning  (10.82 MB)
Chapter 7  Summary  (4.25 MB)
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