WebToolTip com Code with the Author of Build an LLM From Scratch | 1B0592C4D2DBAA7DC770E95FE0C0A773F8A659DD

WebToolTip com Code with the Author of Build an LLM From Scratch

Name	[ WebToolTip.com ] Code with the Author of Build an LLM (From Scratch)	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	3.4 GB
Total Files	50
Last Seen	2025-07-19 00:24
Hash	1B0592C4D2DBAA7DC770E95FE0C0A773F8A659DD

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001. Chapter 1. Python Environment Setup.mp4	94.6 MB
002. Chapter 2. Tokenizing text.mp4	103.9 MB
003. Chapter 2. Converting tokens into token IDs.mp4	41.9 MB
004. Chapter 2. Adding special context tokens.mp4	36.5 MB
005. Chapter 2. Byte pair encoding.mp4	73.1 MB
006. Chapter 2. Data sampling with a sliding window.mp4	96.3 MB
007. Chapter 2. Creating token embeddings.mp4	34.4 MB
008. Chapter 2. Encoding word positions.mp4	51.6 MB
009. Chapter 3. A simple self-attention mechanism without trainable weights Part 1.mp4	182.3 MB
010. Chapter 3. A simple self-attention mechanism without trainable weights Part 2.mp4	57.6 MB
011. Chapter 3. Computing the attention weights step by step.mp4	66.6 MB
012. Chapter 3. Implementing a compact self-attention Python class.mp4	35.3 MB
013. Chapter 3. Applying a causal attention mask.mp4	59.1 MB
014. Chapter 3. Masking additional attention weights with dropout.mp4	17.6 MB
015. Chapter 3. Implementing a compact causal self-attention class.mp4	43.5 MB
016. Chapter 3. Stacking multiple single-head attention layers.mp4	47.8 MB
017. Chapter 3. Implementing multi-head attention with weight splits.mp4	133.2 MB
018. Chapter 4. Coding an LLM architecture.mp4	65.1 MB
019. Chapter 4. Normalizing activations with layer normalization.mp4	88.1 MB
020. Chapter 4. Implementing a feed forward network with GELU activations.mp4	107.0 MB
021. Chapter 4. Adding shortcut connections.mp4	46.4 MB
022. Chapter 4. Connecting attention and linear layers in a transformer block.mp4	67.2 MB
023. Chapter 4. Coding the GPT model.mp4	70.2 MB
024. Chapter 4. Generating text.mp4	68.9 MB
025. Chapter 5. Using GPT to generate text.mp4	75.1 MB
026. Chapter 5. Calculating the text generation loss cross entropy and perplexity.mp4	102.3 MB
027. Chapter 5. Calculating the training and validation set losses.mp4	99.3 MB
028. Chapter 5. Training an LLM.mp4	145.6 MB
029. Chapter 5. Decoding strategies to control randomness.mp4	21.0 MB
030. Chapter 5. Temperature scaling.mp4	44.2 MB
031. Chapter 5. Top-k sampling.mp4	27.5 MB
032. Chapter 5. Modifying the text generation function.mp4	35.1 MB
033. Chapter 5. Loading and saving model weights in PyTorch.mp4	23.1 MB
034. Chapter 5. Loading pretrained weights from OpenAI.mp4	111.8 MB
035. Chapter 6. Preparing the dataset.mp4	108.9 MB
036. Chapter 6. Creating data loaders.mp4	57.0 MB
037. Chapter 6. Initializing a model with pretrained weights.mp4	44.3 MB
038. Chapter 6. Adding a classification head.mp4	77.2 MB
039. Chapter 6. Calculating the classification loss and accuracy.mp4	67.6 MB
040. Chapter 6. Fine-tuning the model on supervised data.mp4	170.6 MB
041. Chapter 6. Using the LLM as a spam classifier.mp4	37.6 MB
042. Chapter 7. Preparing a dataset for supervised instruction fine-tuning.mp4	49.5 MB
043. Chapter 7. Organizing data into training batches.mp4	83.7 MB
044. Chapter 7. Creating data loaders for an instruction dataset.mp4	33.9 MB
045. Chapter 7. Loading a pretrained LLM.mp4	25.9 MB
046. Chapter 7. Fine-tuning the LLM on instruction data.mp4	102.9 MB
047. Chapter 7. Extracting and saving responses.mp4	44.4 MB
048. Chapter 7. Evaluating the fine-tuned LLM.mp4	107.1 MB
Bonus Resources.txt	0.1 KB
Total files 50