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Build a Large Language Model from Scratch (early access), Video Edition

سرفصل های دوره
  • Appendix A. Automatic differentiation made easy
  • Appendix A. A typical training loop
  • Appendix A. Exercise answers
  • Appendix A. Further reading
  • Appendix A. Implementing multilayer neural networks
  • Appendix A. Introduction to PyTorch
  • Appendix A. Optimizing training performance with GPUs
  • Appendix A. Saving and loading models
  • Appendix A. Seeing models as computation graphs
  • Appendix A. Setting up efficient data loaders
  • Appendix A. Summary
  • Appendix A. Understanding tensors
  • Appendix D. Adding Bells and Whistles to the Training Loop
  • Appendix D. Cosine decay
  • Appendix D. Gradient clipping
  • Appendix D. The modified training function
  • Appendix E. Initializing the model
  • Appendix E. Parameter-efficient Finetuning with LoRA
  • Appendix E. Parameter-efficient finetuning with LoRA (1)
  • Appendix E. Preparing the dataset
  • Chapter 1. Applications of LLMs
  • Chapter 1. A closer look at the GPT architecture
  • Chapter 1. Building a large language model
  • Chapter 1. Introducing the transformer architecture
  • Chapter 1. Stages of building and using LLMs
  • Chapter 1. Summary
  • Chapter 1. Understanding Large Language Models
  • Chapter 1. Utilizing large datasets
  • Chapter 2. Adding special context tokens
  • Chapter 2. Byte pair encoding
  • Chapter 2. Converting tokens into token IDs
  • Chapter 2. Creating token embeddings
  • Chapter 2. Data sampling with a sliding window
  • Chapter 2. Encoding word positions
  • Chapter 2. Summary
  • Chapter 2. Tokenizing text
  • Chapter 2. Working with Text Data
  • Chapter 3. Attending to different parts of the input with self-attention
  • Chapter 3. Capturing data dependencies with attention mechanisms
  • Chapter 3. Coding Attention Mechanisms
  • Chapter 3. Extending single-head attention to multi-head attention
  • Chapter 3. Hiding future words with causal attention
  • Chapter 3. Implementing self-attention with trainable weights
  • Chapter 3. Summary
  • Chapter 4. Adding shortcut connections
  • Chapter 4. Coding the GPT model
  • Chapter 4. Connecting attention and linear layers in a transformer block
  • Chapter 4. Generating text
  • Chapter 4. Implementing a GPT model from Scratch To Generate Text
  • Chapter 4. Implementing a feed forward network with GELU activations
  • Chapter 4. Normalizing activations with layer normalization
  • Chapter 4. Summary
  • Chapter 5. Decoding strategies to control randomness
  • Chapter 5. Loading and saving model weights in PyTorch
  • Chapter 5. Loading pretrained weights from OpenAI
  • Chapter 5. Pretraining on Unlabeled Data
  • Chapter 5. Summary
  • Chapter 5. Training an LLM
  • Chapter 6. Adding a classification head
  • Chapter 6. Calculating the classification loss and accuracy
  • Chapter 6. Creating data loaders
  • Chapter 6. Finetuning for Classification
  • Chapter 6. Finetuning the model on supervised data
  • Chapter 6. Initializing a model with pretrained weights
  • Chapter 6. Preparing the dataset
  • Chapter 6. Summary
  • Chapter 6. Using the LLM as a spam classifier
  • Chapter 7. Conclusions
  • Chapter 7. Creating data loaders for an instruction dataset
  • Chapter 7. Evaluating the finetuned LLM
  • Chapter 7. Extracting and saving responses
  • Chapter 7. Finetuning the LLM on instruction data
  • Chapter 7. Finetuning to Follow Instructions
  • Chapter 7. Loading a pretrained LLM
  • Chapter 7. Organizing data into training batches
  • Chapter 7. Preparing a dataset for supervised instruction finetuning
  • Chapter 7. Summary
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    مدت زمان: 492 دقیقه
    تاریخ انتشار: 15 آبان 1403
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