Open-source alternatives to TensorFlow Examples

This project is a collection of educational examples and code for implementing deep learning architectures using the PyTorch framework. It serves as a tutorial and implementation guide for building various neural network architectures for machine learning tasks. The project provides practical implementations for computer vision, including image classification and neural style transfer, as well as natural language processing examples for building sequence models and language predictors. It also covers generative models using adversarial and variational networks to synthesize or transform visua

This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning library. It provides practical examples for building, training, and deploying deep learning models, functioning as a toolkit for developers to explore neural network architectures and training workflows. The project distinguishes itself by offering concrete demonstrations of complex machine learning operations, ranging from computer vision tasks like object detection and depth estimation to the training of large-scale transformer models. These examples illustrate how to implement

This project is a structured educational curriculum designed to guide developers through the fundamentals of machine learning. It functions as a technical skill builder, offering a curated roadmap of progressive coding challenges that cover core algorithms, statistical concepts, and essential data science libraries. The repository distinguishes itself through an iterative sequencing of content, organizing complex technical topics into a daily progression that facilitates incremental mastery. It integrates third-party academic lectures and educational resources to provide necessary theoretical

Grokking-Deep-Learning is a collection of educational resources and courseware designed to teach the construction of neural networks from scratch. It serves as a programming tutorial and implementation guide for understanding the internal mechanics of deep learning. The project focuses on building various network architectures, including convolutional, recurrent, and long short-term memory networks. It provides step-by-step implementations of fundamental mechanisms such as forward propagation, backpropagation, and gradient descent. The material covers a broad range of deep learning capabilit

This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners to master complex artificial intelligence concepts through hands-on experimentation. The platform distinguishes itself by integrating technical explanations with executable Jupyter notebooks. This design allows readers to modify code and hyperparameters in real-time, facilitati

This project is a collection of deep learning tutorials and practical implementations using TensorFlow. It provides a neural network implementation guide through code examples designed for research-oriented deep learning. The repository covers supervised and unsupervised learning workflows, including the development of sequence models for language processing and chatbots. It includes specific examples for image style transfer and the use of autoencoders for feature extraction. The project also provides demonstrations for managing large-scale datasets using binary record formats and streaming

100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms. The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hier

OneFlow is a deep learning framework and distributed execution engine designed for building, training, and deploying neural network architectures. It functions as a scalable neural network library that allows for the development of deep learning models and their execution across distributed hardware. The project includes a machine learning graph compiler used to optimize neural network execution graphs. This allows for the acceleration of model performance and the reduction of latency during both training and inference. The framework covers broad capability areas including large-scale model

This project is a deep learning curriculum and a collection of PyTorch tutorials designed for deep learning education. It provides a structured set of technical documents and runnable notebooks that translate theoretical machine learning concepts into executable code. The repository includes implementation guides for various neural network architectures, specifically covering convolutional, recurrent, and transformer-based models. It provides practical examples for building computer vision pipelines for object detection and semantic segmentation, as well as natural language processing tools f

EffectiveTensorflow is a deep learning tutorial suite and learning resource designed for building models within the TensorFlow framework. It serves as a practical implementation guide and development manual for creating neural network architectures. The project provides curated instructions for prototyping custom operations and implementing conditional logic for recurrent and deep learning structures. It focuses on the transition from imperative prototyping to the optimization of symbolic execution graphs for hardware accelerators. The resource covers numerical stability management to preven

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

:earth_americas: Simple and ready-to-use tutorials for TensorFlow

This project is a comprehensive educational resource and technical documentation suite for learning and developing deep learning models. It serves as an open-source textbook, implementation manual, and framework tutorial designed to guide users through the mathematical foundations and practical application of neural networks. The resource provides detailed instructional content on building various model architectures, including convolutional and recurrent neural networks. It includes a dedicated distributed training guide and a learning path that covers the fundamentals of tensors, automatic

Deeplearnjs is a JavaScript deep learning framework and automatic differentiation engine designed for building and training artificial intelligence models within a web browser environment. It functions as a machine learning library that leverages WebGL to provide hardware acceleration for neural networks. The project serves as a high-performance linear algebra library, using the GPU to execute operations on multi-dimensional arrays. This enables the implementation of deep learning models and the execution of client-side machine learning inference. The framework covers the complete automatic

LLM101n is an educational machine learning curriculum and open-source resource designed to teach the fundamental principles and practical implementation of large language models. It functions as a technical manual that guides users through the end-to-end process of building and training neural network architectures from scratch using a dynamic tensor library for automatic differentiation and GPU-accelerated computation. The project distinguishes itself through interactive, notebook-based instruction that allows for real-time visualization of training processes. It supports rapid experimentati

This project is a collaborative academic repository designed for the synthesis of research papers and the study of machine learning architectures. It functions as a technical knowledge base, providing curated reading paths and annotated summaries to help students and practitioners master complex topics in artificial intelligence, computer vision, and natural language processing. The repository utilizes a static site generation model to transform structured text files into a navigable documentation site. Content is organized through hierarchical directory routing, which maps the repository's f

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili

Tinygrad is a deep learning framework and tensor computation engine designed for building and training neural networks. It functions as a hardware abstraction layer that manages device memory, command queues, and kernel dispatching across heterogeneous computing architectures. By utilizing a lazy-evaluation approach, the framework constructs computational graphs that defer execution until data is explicitly required, allowing it to process only the necessary operations for a given result. The project distinguishes itself through a just-in-time compilation layer that transforms abstract comput

CNTK is a deep learning toolkit used for the design, construction, and training of neural networks. It defines model architectures as computational graphs and optimizes network parameters using an automatic differentiation engine and stochastic gradient descent. The project emphasizes large scale model distribution, spreading training workloads across multiple hardware nodes and GPUs. It features specialized support for dynamic sequence handling, allowing filters to be convolved across both spatial and dynamic sequence axes to process data of variable lengths. The toolkit provides hardware-a

This repository is the official documentation for TensorFlow, a machine learning framework. It provides comprehensive guides, tutorials, and API references for building, training, and deploying machine learning models. The documentation covers the full lifecycle of machine learning projects, from constructing data pipelines and building neural networks with high-level APIs to customizing training loops and deploying trained models in production, on edge devices, or in browsers. The documentation includes step-by-step tutorials for a range of tasks, including reinforcement learning, ranking mo

Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co

ConvNetJS is a JavaScript deep learning library and neural network training engine designed for client-side machine learning. It functions as a framework for building, training, and running convolutional neural networks directly within a web browser without the need for a backend server. The library specializes in image recognition and pattern analysis using convolutional and pooling layers. It enables the creation of models for classification and regression tasks, as well as the development of reinforcement learning agents that optimize behavior through trial and error in simulated environme

TensorFlow - A curated list of dedicated resources http://tensorflow.org

深度学习入门教程, 优秀文章, Deep Learning Tutorial

This project is a community-driven academic resource index and knowledge base dedicated to the study of transfer learning and domain adaptation. It functions as a curated repository of scholarly materials, including academic papers, tutorials, datasets, and benchmarks, designed to support research into how machine learning models apply knowledge from one task to another. The repository organizes these resources into a hierarchical taxonomy to facilitate the discovery of specialized methodologies. By leveraging distributed version control, the project maintains an evolving archive of research