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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
learningggggggg 🐳
| Date | Stars |
|---|---|
| 2026-07-31 | 634 |
| 2026-08-06 | 634 |
| 2026-08-07 | 634 |
| 2026-08-09 | 635 |
| 2026-08-14 | 635 |
| 2026-08-15 | 635 |
| 2026-08-17 | 634 |
| 2026-08-18 | 634 |
| 2026-08-22 | 634 |
| 2026-08-24 | 635 |
| 2026-09-01 | 639 |
| 2026-10-16 | 639 |
| 2026-11-04 | 639 |
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# Deep Learning Research Paper Collection ## Overview This repository is a collection of IMPORTANT deep learning research papers, organized by research area and implementation. The goal is to provide a structured approach to understanding the evolution and core concepts of deep learning. ## Disclaimer > [!IMPORTANT] > This is a personal learning project. The implementations and notes may contain errors or simplifications. Use with caution and always refer to the original papers. ## Inspiration and Credits Inspired by [adam-maj](https://github.com/adam-maj) and expanded with additional research papers and implementations. ## Project Goals - Implement approximately 60 important deep learning papers - Provide scratch implementations for learning and understanding - Create a comprehensive resource for deep learning research ## Contents ### 1. Foundational Deep Neural Networks #### Papers - **DNN** (1987): Learning Internal Representations by Error Propagation [pdf](https://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf) - **CNN** (1989): Backpropagation Applied to Handwritten Zip Code Recognition [pdf](http://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf) - **LeNet** (1998): Gradient-Based Learning Applied to Document Recognition [pdf](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf) - **AlexNet** (2012): ImageNet Classification with Deep Convolutional Networks [pdf](https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf) - **U-Net** (2015): Convolutional Networks for Biomedical Image Segmentation [pdf](https://arxiv.org/pdf/1505.04597.pdf) ### 2. Optimization and Regularization Techniques #### Papers - **Weight Decay** (1991): A Simple Weight Decay Can Improve Generalization [pdf](https://www.cs.toronto.edu/~hinton/absps/nips93.pdf) - **ReLU** (2011): Deep Sparse Rectified Neural Networks [pdf](https://www.cs.toronto.edu/~hinton/absps/reluICML.pdf) - **Residuals** (2015): Deep Residual Learning for Image Recognition [pdf](https://arxiv.org/pdf/1512.03385.pdf) - **Dropout** (2014): Preventing Neural Networks from Overfitting [pdf](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) - **BatchNorm** (2015): Accelerating Deep Network Training [pdf](https://arxiv.org/pdf/1502.03167.pdf) - **LayerNorm** (2016): Layer Normalization [pdf](https://arxiv.org/pdf/1607.06450.pdf) - **GELU** (2016): Gaussian Error Linear Units [pdf](https://arxiv.org/pdf/1606.08415.pdf) - **Adam** (2014): Stochastic Optimization Method [pdf](https://arxiv.org/pdf/1412.6980.pdf) ### 3. Sequence Modeling #### Papers - **RNN** (1989): Continually Running Fully Recurrent Neural Networks [pdf](https://www.bioinf.jku.at/publications/older/2604.pdf) - **LSTM** (1997): Long-Short Term Memory [pdf](https://www.bioinf.jku.at/publications/older/2308.pdf) - **Learning to Forget** (2000): Continual Prediction with LSTM [pdf](https://www.researchgate.net/publication/221601044_Learning_to_Forget_Continual_Prediction_with_LSTM) - **Word2Vec** (2013): Word Representations in Vector Space [pdf](https://arxiv.org/pdf/1301.3781.pdf) - **Phrase2Vec** (2013): Distributed Representations of Words and Phrases [pdf](https://arxiv.org/pdf/1310.4546.pdf) - **Encoder-Decoder** (2014): RNN Encoder-Decoder for Machine Translation [pdf](https://arxiv.org/pdf/1406.1078.pdf) - **Seq2Seq** (2014): Sequence to Sequence Learning [pdf](https://arxiv.org/pdf/1409.3215.pdf) - **Attention** (2014): Neural Machine Translation with Alignment [pdf](https://arxiv.org/pdf/1409.0473.pdf) - **Mixture of Experts** (2017): Sparsely-Gated Neural Networks [pdf](https://arxiv.org/pdf/1701.06538.pdf) ### 4. Language Modeling #### Papers - **Transformer** (2017): Attention Is All You Need [pdf](https://arxiv.org/pdf/1706.03762.pdf) - **BERT** (2018): Bidirectional Transformers for Language Understanding [pdf](https://arxiv.org/pdf/1810.04805.pdf) - **RoBERTa** (2019): Robustly Optimized BERT Pretraining [pdf](https://arxiv.org/pdf/1
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matched fp:40abbad29961ad63, llm:Repository name 'History-of-Deep-Learning' suggests educational/learning material about deep learning; description 'learningggggggg 🐳' indicates learning resource. No topics or readme provided.