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Deep Learning: Frameworks, Datasets, GNNs & Learning Resources

Deep learning uses multi-layer neural networks to learn representations from raw data. This hub links to framework, dataset and GNN directories and to the main libraries, courses and books.

46 topics•5 categories•Updated November 2026

Deep Learning Directories on This Site

20 entries

Frameworks, GNNs and data.

01Top Deep Learning AlgorithmsANN, CNN, RNN, LSTM, GRU, autoencoder, GAN, Transformer, BERT and GPT, with the original papers. 02Deep Learning FrameworksTensorFlow, PyTorch, Keras, JAX, MXNet, PaddlePaddle and more. 03Graph Neural Network (GNN) FrameworksFrameworks for learning on graph-structured data. 04Graph Neural NetworksGCN, GAT, GraphSAGE and GIN explained. 05Data Sets for Machine & Deep LearningBenchmark and production datasets. 06Deep Learning PlatformsPlatforms for deep learning. 07Python Frameworks for AIPython frameworks for AI development. 08Generative Adversarial Networks (GANs)GAN papers, architectures such as StyleGAN, code and applications. 09Variational Autoencoders (VAEs)VAE theory, VQ-VAE, latent spaces, code and tutorials. 10Diffusion ModelsDDPM, Stable Diffusion, flow matching, image and video models, and libraries. 11Vision Language ModelsOpen and proprietary vision language models, encoders and benchmarks. 12Vision TransformersHow ViTs split images into patches, plus papers, models and libraries. 13Generative AI ArchitecturesTransformers, diffusion models, GANs, VAEs and state space models, and how they compare. 14Large Language ModelsFrontier and open-weight large language models. 15Multimodal Language ModelsModels that handle text, images, audio and video. 16Large Reasoning ModelsTest-time reasoning models that plan, verify and think before answering. 17Spiking Neural NetworksBrain-inspired models that communicate with spikes. 18Time-Series Foundation ModelsPretrained models for forecasting and time series. 19AI Model Quantization TechniquesGPTQ, AWQ and other quantization methods. 20Open-Source LLM Inference EnginesvLLM, SGLang and other serving engines.

Core Frameworks

9 entries

Where most deep learning is written.

21PyTorchOpen-source deep learning framework widely used in research and production. 22TensorFlowGoogle's end-to-end machine learning platform. 23JAXComposable transformations of Python and NumPy programs for high-performance ML. 24KerasHigh-level deep learning API that runs on several backends. 25ONNXOpen format for exchanging models between frameworks. 26Hugging Face TransformersLibrary and hub for pretrained transformer models across text, vision, audio and multimodal tasks. 27PyTorch LightningHigh-level PyTorch framework that organizes training code and scales across GPUs. 28FlaxNeural network library for JAX, designed for flexibility and research. 29MLXApple's array framework for machine learning on Apple silicon.

Courses & Books

11 entries

Learn the theory and practice.

30Dive into Deep LearningFree interactive deep learning textbook with code. 31Deep Learning (Goodfellow, Bengio, Courville)Free online edition of the standard textbook. 32fast.aiFree practical deep learning courses. 33Understanding Deep LearningFree textbook on deep learning. 34Neural Networks: Zero to Hero (Andrej Karpathy)Video series building neural networks and language models from scratch. 35MIT 6.S191: Introduction to Deep LearningMIT introduction to deep learning on OpenCourseWare. 36Hugging Face LearnFree courses on NLP, LLMs and agents. 37Deep Learning Courses and CertificationsDeep learning courses, tutorials and certifications. 38PyTorch TutorialsOfficial tutorials for learning PyTorch and deep learning workflows. 39Stanford CS231nDeep learning for computer vision course. 40Stanford CS224NNLP with deep learning course.

Book from the Author

1 entry

Edge deployment of deep learning models.

41Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native ApplicationsCo-authored by Gayathri and Pethuru Raj (Taylor & Francis); covers model compression, quantization, pruning, knowledge distillation and edge deployment.

Related Pages

5 entries

Neighbouring directories.

42Machine LearningMachine learning hub. 43Mathematics for Deep LearningHub for the mathematics used in deep learning: linear algebra, calculus, optimisation, probability and numerical computing. 44Evaluation Metrics for Deep LearningClassification, regression, vision, language and LLM metrics, benchmarks and libraries. 45Deployment Strategies for Deep Learning ModelsServing modes, rollout strategies, model servers, optimisation, edge and LLM deployment. 46Techniques for AI Model OptimizationPruning, quantisation, distillation, low-rank methods, efficient architectures and inference-time techniques.

Frequently Asked Questions

What is deep learning?

Deep learning is a branch of machine learning that uses neural networks with many layers to learn features directly from raw data such as images, text and audio.

Which framework should I learn first?

PyTorch is the most common in research and increasingly in production; TensorFlow and Keras remain widely used, and JAX suits high-performance research.

What are graph neural networks?

Networks that learn from graph-structured data such as social networks and molecules; see the GNN Frameworks and Graph Neural Networks pages.

How do I learn deep learning?

Work through Dive into Deep Learning or fast.ai alongside a framework, and read the original papers once you have the basics.

How do I deploy models on edge devices?

Compress them with quantization, pruning and distillation; the author's book listed here covers these techniques.