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

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

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001. Chapter 1. Python Environment Setup.mp4	MP4	90.2 MB
002. Chapter 2. Tokenizing text.mp4	MP4	99.1 MB
003. Chapter 2. Converting tokens into token IDs.mp4	MP4	40 MB
004. Chapter 2. Adding special context tokens.mp4	MP4	34.8 MB
005. Chapter 2. Byte pair encoding.mp4	MP4	69.7 MB
006. Chapter 2. Data sampling with a sliding window.mp4	MP4	91.9 MB
007. Chapter 2. Creating token embeddings.mp4	MP4	32.8 MB
008. Chapter 2. Encoding word positions.mp4	MP4	49.2 MB
009. Chapter 3. A simple self-attention mechanism without trainable weights Part 1.mp4	MP4	173.9 MB
010. Chapter 3. A simple self-attention mechanism without trainable weights Part 2.mp4	MP4	55 MB
011. Chapter 3. Computing the attention weights step by step.mp4	MP4	63.5 MB
012. Chapter 3. Implementing a compact self-attention Python class.mp4	MP4	33.6 MB
013. Chapter 3. Applying a causal attention mask.mp4	MP4	56.4 MB
014. Chapter 3. Masking additional attention weights with dropout.mp4	MP4	16.8 MB
015. Chapter 3. Implementing a compact causal self-attention class.mp4	MP4	41.5 MB
016. Chapter 3. Stacking multiple single-head attention layers.mp4	MP4	45.5 MB
017. Chapter 3. Implementing multi-head attention with weight splits.mp4	MP4	127.1 MB
018. Chapter 4. Coding an LLM architecture.mp4	MP4	62.1 MB
019. Chapter 4. Normalizing activations with layer normalization.mp4	MP4	84 MB
020. Chapter 4. Implementing a feed forward network with GELU activations.mp4	MP4	102.1 MB
021. Chapter 4. Adding shortcut connections.mp4	MP4	44.3 MB
022. Chapter 4. Connecting attention and linear layers in a transformer block.mp4	MP4	64.1 MB
023. Chapter 4. Coding the GPT model.mp4	MP4	67 MB
024. Chapter 4. Generating text.mp4	MP4	65.7 MB
025. Chapter 5. Using GPT to generate text.mp4	MP4	71.6 MB
026. Chapter 5. Calculating the text generation loss cross entropy and perplexity.mp4	MP4	97.6 MB
027. Chapter 5. Calculating the training and validation set losses.mp4	MP4	94.7 MB
028. Chapter 5. Training an LLM.mp4	MP4	138.8 MB
029. Chapter 5. Decoding strategies to control randomness.mp4	MP4	20.1 MB
030. Chapter 5. Temperature scaling.mp4	MP4	42.2 MB
031. Chapter 5. Top-k sampling.mp4	MP4	26.3 MB
032. Chapter 5. Modifying the text generation function.mp4	MP4	33.5 MB
033. Chapter 5. Loading and saving model weights in PyTorch.mp4	MP4	22 MB
034. Chapter 5. Loading pretrained weights from OpenAI.mp4	MP4	106.6 MB
035. Chapter 6. Preparing the dataset.mp4	MP4	103.8 MB
036. Chapter 6. Creating data loaders.mp4	MP4	54.3 MB
037. Chapter 6. Initializing a model with pretrained weights.mp4	MP4	42.3 MB
038. Chapter 6. Adding a classification head.mp4	MP4	73.7 MB
039. Chapter 6. Calculating the classification loss and accuracy.mp4	MP4	64.5 MB
040. Chapter 6. Fine-tuning the model on supervised data.mp4	MP4	162.7 MB
041. Chapter 6. Using the LLM as a spam classifier.mp4	MP4	35.9 MB
042. Chapter 7. Preparing a dataset for supervised instruction fine-tuning.mp4	MP4	47.2 MB
043. Chapter 7. Organizing data into training batches.mp4	MP4	79.8 MB
044. Chapter 7. Creating data loaders for an instruction dataset.mp4	MP4	32.3 MB
045. Chapter 7. Loading a pretrained LLM.mp4	MP4	24.7 MB
046. Chapter 7. Fine-tuning the LLM on instruction data.mp4	MP4	98.2 MB
047. Chapter 7. Extracting and saving responses.mp4	MP4	42.3 MB
048. Chapter 7. Evaluating the fine-tuned LLM.mp4	MP4	102.1 MB
Bonus Resources.txt	TXT	102.4 B
Get Bonus Downloads Here.url	URL	204.8 B

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

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