My implementation of Machine Learning and Deep Learning papers from scratch.
Paper Name | Link to Paper | Year Published | GitHub Folder |
---|---|---|---|
Improving Language Understanding by Generative Pre-Training | GPT Paper | 2018 | GPT Implementation |
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | BERT Paper | 2019 | BERT Implementation |
Language Models are Unsupervised Multitask Learners | GPT2 Paper | 2019 | GPT2 Implementation |
LoRA: Low-Rank Adaptation of Large Language Models | LoRA Paper | 2021 | LoRA Implementation |
List of resources, that I found helpful while understanding and coding the concepts.
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Attention is all you need (Transformer) - Model explanation (including math), Inference and Training by Umar jamil: Youtube.
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Coding a Transformer from scratch on PyTorch, with full explanation, training and inference by Umar jamil: Youtube.
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Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy. Youtube.
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Formal Algorithms for Transformers. arXiv