Build a Large Language Model from Scratch (early access), Video Edition
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Appendix A. Automatic differentiation made easy.mp4
03:54
Appendix A. A typical training loop.mp4
07:25
Appendix A. Exercise answers.mp4
01:45
Appendix A. Further reading.mp4
02:08
Appendix A. Implementing multilayer neural networks.mp4
08:47
Appendix A. Introduction to PyTorch.mp4
15:16
Appendix A. Optimizing training performance with GPUs.mp4
17:11
Appendix A. Saving and loading models.mp4
01:50
Appendix A. Seeing models as computation graphs.mp4
02:26
Appendix A. Setting up efficient data loaders.mp4
08:19
Appendix A. Summary.mp4
01:21
Appendix A. Understanding tensors.mp4
07:20
Appendix D. Adding Bells and Whistles to the Training Loop.mp4
02:58
Appendix D. Cosine decay.mp4
01:46
Appendix D. Gradient clipping.mp4
03:16
Appendix D. The modified training function.mp4
01:29
Appendix E. Initializing the model.mp4
01:14
Appendix E. Parameter-efficient Finetuning with LoRA.mp4
04:44
Appendix E. Parameter-efficient finetuning with LoRA (1).mp4
09:04
Appendix E. Preparing the dataset.mp4
01:30
Chapter 1. Applications of LLMs.mp4
02:11
Chapter 1. A closer look at the GPT architecture.mp4
05:23
Chapter 1. Building a large language model.mp4
01:49
Chapter 1. Introducing the transformer architecture.mp4
07:02
Chapter 1. Stages of building and using LLMs.mp4
04:33
Chapter 1. Summary.mp4
02:04
Chapter 1. Understanding Large Language Models.mp4
08:35
Chapter 1. Utilizing large datasets.mp4
03:53
Chapter 2. Adding special context tokens.mp4
05:57
Chapter 2. Byte pair encoding.mp4
04:44
Chapter 2. Converting tokens into token IDs.mp4
05:38
Chapter 2. Creating token embeddings.mp4
05:25
Chapter 2. Data sampling with a sliding window.mp4
09:15
Chapter 2. Encoding word positions.mp4
07:09
Chapter 2. Summary.mp4
01:51
Chapter 2. Tokenizing text.mp4
06:07
Chapter 2. Working with Text Data.mp4
08:09
Chapter 3. Attending to different parts of the input with self-attention.mp4
15:20
Chapter 3. Capturing data dependencies with attention mechanisms.mp4
03:01
Chapter 3. Coding Attention Mechanisms.mp4
06:43
Chapter 3. Extending single-head attention to multi-head attention.mp4
14:12
Chapter 3. Hiding future words with causal attention.mp4
12:44
Chapter 3. Implementing self-attention with trainable weights.mp4
17:54
Chapter 3. Summary.mp4
01:44
Chapter 4. Adding shortcut connections.mp4
05:36
Chapter 4. Coding the GPT model.mp4
09:28
Chapter 4. Connecting attention and linear layers in a transformer block.mp4
05:37
Chapter 4. Generating text.mp4
08:19
Chapter 4. Implementing a GPT model from Scratch To Generate Text.mp4
11:53
Chapter 4. Implementing a feed forward network with GELU activations.mp4
06:51
Chapter 4. Normalizing activations with layer normalization.mp4
10:30
Chapter 4. Summary.mp4
01:29
Chapter 5. Decoding strategies to control randomness.mp4
12:10
Chapter 5. Loading and saving model weights in PyTorch.mp4
03:30
Chapter 5. Loading pretrained weights from OpenAI.mp4
08:59
Chapter 5. Pretraining on Unlabeled Data.mp4
29:18
Chapter 5. Summary.mp4
01:10
Chapter 5. Training an LLM.mp4
07:44
Chapter 6. Adding a classification head.mp4
09:57
Chapter 6. Calculating the classification loss and accuracy.mp4
05:25
Chapter 6. Creating data loaders.mp4
07:21
Chapter 6. Finetuning for Classification.mp4
04:59
Chapter 6. Finetuning the model on supervised data.mp4
06:12
Chapter 6. Initializing a model with pretrained weights.mp4
02:18
Chapter 6. Preparing the dataset.mp4
04:16
Chapter 6. Summary.mp4
01:25
Chapter 6. Using the LLM as a spam classifier.mp4
01:50
Chapter 7. Conclusions.mp4
03:22
Chapter 7. Creating data loaders for an instruction dataset.mp4
03:53
Chapter 7. Evaluating the finetuned LLM.mp4
10:22
Chapter 7. Extracting and saving responses.mp4
06:00
Chapter 7. Finetuning the LLM on instruction data.mp4
07:06
Chapter 7. Finetuning to Follow Instructions.mp4
03:42
Chapter 7. Loading a pretrained LLM.mp4
04:34
Chapter 7. Organizing data into training batches.mp4
15:09
Chapter 7. Preparing a dataset for supervised instruction finetuning.mp4
05:15
Chapter 7. Summary.mp4
01:14
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- language english
- Training sessions 77
- duration 8:12:00
- Release Date 2024/11/03
