A curated list of awesome frameworks, libraries, tools, tutorials, research papers, and resources for deep learning. This list covers neural networks, model optimization, NLP, computer vision, and other deep learning applications.
- Frameworks and Libraries
- Tools and Utilities
- Neural Network Architectures
- Optimization and Training
- Natural Language Processing (NLP)
- Computer Vision
- Generative Models
- Learning Resources
- Research Papers
- Books
- Community
- Contribute
- License
- TensorFlow - An end-to-end open-source platform for machine learning and deep learning.
- PyTorch - A popular open-source deep learning framework that offers dynamic computation graphs.
- Keras - A high-level neural networks API, running on top of TensorFlow.
- MXNet - A deep learning framework known for its efficiency and scalability.
- JAX - A library for high-performance numerical computing and automatic differentiation.
- Caffe - A deep learning framework focused on convolutional neural networks (CNNs).
- Theano - A historical deep learning library for mathematical computations, now deprecated but influential.
- TensorBoard - A visualization toolkit for TensorFlow.
- Weights & Biases - A tool for experiment tracking, model monitoring, and hyperparameter optimization.
- PyTorch Lightning - A lightweight PyTorch wrapper for scalable deep learning.
- DeepSpeed - An optimization library for training large deep learning models.
- ONNX - An open format to represent deep learning models, enabling interoperability across frameworks.
- Convolutional Neural Networks (CNNs) - A popular architecture for image and video analysis.
- Recurrent Neural Networks (RNNs) - A neural network architecture for sequence data, such as time series and text.
- Long Short-Term Memory (LSTM) - A special type of RNN capable of learning long-term dependencies.
- Transformers - The architecture that introduced self-attention mechanisms and revolutionized NLP.
- Autoencoders - Neural networks designed for unsupervised learning of efficient codings.
- Graph Neural Networks (GNNs) - A type of neural network for learning from graph-structured data.
- Adam Optimizer - An adaptive learning rate optimization algorithm.
- Stochastic Gradient Descent (SGD) - A popular optimization method for training deep learning models.
- Batch Normalization - A technique to stabilize and accelerate the training of deep networks.
- Dropout - A regularization technique to prevent neural networks from overfitting.
- Learning Rate Schedulers - Techniques to adjust the learning rate during training for better convergence.
- Hugging Face Transformers - A library for state-of-the-art NLP models like BERT, GPT, and RoBERTa.
- spaCy - An NLP library for fast processing of text data.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) - A transformer model for NLP tasks.
- GPT-3: Language Models are Few-Shot Learners (2020) - A large-scale generative language model.
- Seq2Seq Models - A neural network architecture for sequence-to-sequence learning tasks.
- YOLO (You Only Look Once) - A state-of-the-art real-time object detection system.
- ResNet: Deep Residual Learning for Image Recognition (2015) - A neural network architecture known for its deep residual learning approach.
- VGGNet - A convolutional neural network known for its simplicity and performance in image classification.
- DeepLab - A model for semantic image segmentation.
- Detectron2 - A high-performance framework for object detection and segmentation.
- GANs: Generative Adversarial Networks (2014) - A model architecture for generating realistic data.
- BigGAN: Large-Scale GAN Training for High-Fidelity Natural Image Synthesis (2018) - A generative model for producing high-resolution images.
- VAE: Variational Autoencoders (2013) - A model architecture for generating data through variational inference.
- StyleGAN - A GAN model for high-quality image synthesis.
- Diffusion Models - A generative model framework for image synthesis.
- Deep Learning Specialization on Coursera - A series of courses by Andrew Ng on deep learning.
- Stanford CS230: Deep Learning - A comprehensive course on deep learning.
- The Deep Learning Book - A foundational book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- PyTorch Tutorials - Official tutorials for learning deep learning with PyTorch.
- TensorFlow Tutorials - Official TensorFlow tutorials for building deep learning models.
- Attention Is All You Need (2017) - The paper that introduced the Transformer architecture.
- Deep Residual Learning for Image Recognition (2015) - The introduction of ResNet.
- Generative Adversarial Nets (2014) - Ian Goodfellow’s original GAN paper.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - A comprehensive textbook on deep learning.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron - A practical guide to deep learning.
- Neural Networks and Deep Learning by Michael Nielsen - An introduction to deep learning.
- Reddit: r/MachineLearning - A subreddit for discussing machine learning and deep learning.
- PyTorch Forums - A forum for discussing PyTorch-related topics.
- TensorFlow Community - A place for TensorFlow users to connect.
Contributions are welcome!
