Code with the Author of Build an LLM (From Scratch)

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• 001. Chapter 1. Python Environment Setup.mp4 (90.2 MB)
• 002. Chapter 2. Tokenizing text.mp4 (99.1 MB)
• 003. Chapter 2. Converting tokens into token IDs.mp4 (40.0 MB)
• 004. Chapter 2. Adding special context tokens.mp4 (34.8 MB)
• 005. Chapter 2. Byte pair encoding.mp4 (69.7 MB)
• 006. Chapter 2. Data sampling with a sliding window.mp4 (91.9 MB)
• 007. Chapter 2. Creating token embeddings.mp4 (32.8 MB)
• 008. Chapter 2. Encoding word positions.mp4 (49.2 MB)
• 009. Chapter 3. A simple self-attention mechanism without trainable weights Part 1.mp4 (173.9 MB)
• 010. Chapter 3. A simple self-attention mechanism without trainable weights Part 2.mp4 (55.0 MB)
• 011. Chapter 3. Computing the attention weights step by step.mp4 (63.5 MB)
• 012. Chapter 3. Implementing a compact self-attention Python class.mp4 (33.6 MB)
• 013. Chapter 3. Applying a causal attention mask.mp4 (56.4 MB)
• 014. Chapter 3. Masking additional attention weights with dropout.mp4 (16.8 MB)
• 015. Chapter 3. Implementing a compact causal self-attention class.mp4 (41.5 MB)
• 016. Chapter 3. Stacking multiple single-head attention layers.mp4 (45.5 MB)
• 017. Chapter 3. Implementing multi-head attention with weight splits.mp4 (127.1 MB)
• 018. Chapter 4. Coding an LLM architecture.mp4 (62.1 MB)
• 019. Chapter 4. Normalizing activations with layer normalization.mp4 (84.0 MB)
• 020. Chapter 4. Implementing a feed forward network with GELU activations.mp4 (102.1 MB)
• 021. Chapter 4. Adding shortcut connections.mp4 (44.3 MB)
• 022. Chapter 4. Connecting attention and linear layers in a transformer block.mp4 (64.1 MB)
• 023. Chapter 4. Coding the GPT model.mp4 (67.0 MB)
• 024. Chapter 4. Generating text.mp4 (65.7 MB)
• 025. Chapter 5. Using GPT to generate text.mp4 (71.6 MB)
• 026. Chapter 5. Calculating the text generation loss cross entropy and perplexity.mp4 (97.6 MB)
• 027. Chapter 5. Calculating the training and validation set losses.mp4 (94.7 MB)
• 028. Chapter 5. Training an LLM.mp4 (138.8 MB)
• 029. Chapter 5. Decoding strategies to control randomness.mp4 (20.1 MB)
• 030. Chapter 5. Temperature scaling.mp4 (42.2 MB)
• 031. Chapter 5. Top-k sampling.mp4 (26.3 MB)
• 032. Chapter 5. Modifying the text generation function.mp4 (33.5 MB)
• 033. Chapter 5. Loading and saving model weights in PyTorch.mp4 (22.0 MB)
• 034. Chapter 5. Loading pretrained weights from OpenAI.mp4 (106.6 MB)
• 035. Chapter 6. Preparing the dataset.mp4 (103.8 MB)
• 036. Chapter 6. Creating data loaders.mp4 (54.3 MB)
• 037. Chapter 6. Initializing a model with pretrained weights.mp4 (42.3 MB)
• 038. Chapter 6. Adding a classification head.mp4 (73.7 MB)
• 039. Chapter 6. Calculating the classification loss and accuracy.mp4 (64.5 MB)
• 040. Chapter 6. Fine-tuning the model on supervised data.mp4 (162.7 MB)
• 041. Chapter 6. Using the LLM as a spam classifier.mp4 (35.9 MB)
• 042. Chapter 7. Preparing a dataset for supervised instruction fine-tuning.mp4 (47.2 MB)
• 043. Chapter 7. Organizing data into training batches.mp4 (79.8 MB)
• 044. Chapter 7. Creating data loaders for an instruction dataset.mp4 (32.3 MB)
• 045. Chapter 7. Loading a pretrained LLM.mp4 (24.7 MB)
• 046. Chapter 7. Fine-tuning the LLM on instruction data.mp4 (98.2 MB)
• 047. Chapter 7. Extracting and saving responses.mp4 (42.3 MB)
• 048. Chapter 7. Evaluating the fine-tuned LLM.mp4 (102.1 MB)
• Bonus Resources.txt (0.1 KB)