Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-deep-learning
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.
https://github.com/awesomelistsio/awesome-deep-learning
Last synced: 3 days ago
JSON representation
-
Optimization and Training
- Stochastic Gradient Descent (SGD) - A popular optimization method for training deep learning models.
- 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.
- 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.
- Stochastic Gradient Descent (SGD) - A popular optimization method for training deep learning models.
- Stochastic Gradient Descent (SGD) - A popular optimization method for training deep learning models.
-
Tools and Utilities
- ONNX - An open format to represent deep learning models, enabling interoperability across frameworks.
- TensorBoard - A visualization toolkit for TensorFlow.
- DeepSpeed - An optimization library for training large deep learning models.
- ONNX - An open format to represent deep learning models, enabling interoperability across frameworks.
-
Frameworks and Libraries
- 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.
- Caffe - A deep learning framework focused on convolutional neural networks (CNNs).
- Theano - A historical deep learning library for mathematical computations, now deprecated but influential.
- Caffe - A deep learning framework focused on convolutional neural networks (CNNs).
-
Neural Network Architectures
- Long Short-Term Memory (LSTM) - A special type of RNN capable of learning long-term dependencies.
- 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.
- Graph Neural Networks (GNNs) - A type of neural network for learning from graph-structured data.
-
Learning Resources
- Stanford CS230: Deep Learning - A comprehensive course on deep learning.
- 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.
- PyTorch Tutorials - Official tutorials for learning deep learning with PyTorch.
- TensorFlow Tutorials - Official TensorFlow tutorials for building deep learning models.
- The Deep Learning Book - A foundational book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
-
Natural Language Processing (NLP)
- 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.
- spaCy - An NLP library for fast processing of text data.
-
Computer Vision
- YOLO (You Only Look Once) - A state-of-the-art real-time object detection system.
- 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.
-
Generative Models
- VAE: Variational Autoencoders (2013) - A model architecture for generating data through variational inference.
- StyleGAN - A GAN model for high-quality image synthesis.
- BigGAN: Large-Scale GAN Training for High-Fidelity Natural Image Synthesis (2018) - A generative model for producing high-resolution images.
- Diffusion Models - A generative model framework for image synthesis.
-
Research Papers
- Generative Adversarial Nets (2014) - Ian Goodfellow’s original GAN paper.
- Attention Is All You Need (2017) - The paper that introduced the Transformer architecture.
- Deep Residual Learning for Image Recognition (2015) - The introduction of ResNet.
-
Community
- 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.
- Reddit: r/MachineLearning - A subreddit for discussing machine learning and deep learning.
Programming Languages
Categories
Sub Categories