Neural Networks
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An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
A Python package for tensor computation with GPU acceleration and dynamic neural networks built on a tape-based autograd system.
A collection of 60+ annotated PyTorch implementations of deep learning papers with side-by-side explanatory notes.
A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A deep learning toolkit for Text-to-Speech generation with pretrained models in over 1100 languages and tools for training.
A comprehensive collection of TensorFlow tutorials and examples for beginners, covering both TF v1 and v2 with clear explanations.
A fast open framework for deep learning with a focus on expression, speed, and modularity.
A collection of concise PyTorch tutorials for deep learning researchers, with most models implemented in under 30 lines of code.
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A curated list of awesome deep learning tutorials, projects, and communities.
A curated list of awesome deep learning tutorials, projects, and communities.
LaTeX code and Python interface for creating publication-quality neural network architecture diagrams.
Python implementations of popular machine learning algorithms from scratch with interactive Jupyter demos and mathematical explanations.
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
An open standard format for representing machine learning models to enable interoperability between frameworks.
A minimalist, high-performance machine learning framework for Rust with a focus on serverless inference and GPU support.
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
A research project exploring machine learning for generating music, images, and art using deep learning and reinforcement learning.
A hardware-accelerated JavaScript library for training and deploying machine learning models in the browser and Node.js.
A hardware-accelerated JavaScript library for training and deploying machine learning models in browsers and Node.js.
A topic-wise curated list of machine learning and deep learning tutorials, articles, and resources for developers and data scientists.
A curated collection of tutorials, articles, and resources for learning machine learning and deep learning topics.
A PyTorch library providing datasets, model architectures, and image transformations for computer vision tasks.
Code samples and implementations from the book 'Neural Networks and Deep Learning' for educational purposes.
A unified deep learning toolkit for describing neural networks as computational graphs, supporting feed-forward DNNs, CNNs, and RNNs/LSTMs.
A curated list of awesome TensorFlow experiments, libraries, projects, tutorials, and resources.
A curated list of awesome TensorFlow experiments, libraries, projects, tutorials, and resources.
A comprehensive collection of machine learning algorithms implemented exclusively in NumPy for educational purposes and prototyping.
Lecture materials and slides for the University of Oxford's 2017 advanced course on Deep Natural Language Processing.
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