#TensorFlow

Sonnet, created by DeepMind researchers, provides a flexible programming structure for machine learning advancements using TensorFlow 2. It emphasizes modularity with `snt.Module`, aiding in the development of neural networks adaptable to various learning forms. Sonnet supports both predefined modules and custom-built ones, such as `snt.Linear`, `snt.Conv2D`, and `snt.nets.MLP`. While lacking an integrated training framework, it empowers users to leverage existing solutions or create new ones, supporting distributed learning. Simple installation and illustrative examples on Google Colab make Sonnet accessible for constructing complex machine learning models.

Explore TensorLayer, a versatile deep learning library crafted for both researchers and engineers. It emphasizes flexibility, simplicity, and performance, and is compatible with frameworks like TensorFlow and PyTorch. TensorLayer offers an extensive array of neural layers, comprehensive tutorials, and applications powered by a vibrant community acknowledged by the ACM Multimedia Society. It facilitates swift development of complex AI models, supports diverse hardware, and offers both high-level and professional APIs, with rich multilingual documentation and numerous examples available.

This open-source project uses TensorFlow and Sonnet to enhance optimization with learning-to-learn strategies, focusing on command-line tools for efficient problem-solving, including MNIST and CIFAR10 classifications. It supports quadratic functions and employs advanced optimizers such as L2L. The flexible design allows easy integration of new problems and adjustment of parameters like learning rate and epochs, effectively showcasing TensorFlow's optimization capabilities across diverse challenges. Not officially affiliated with Google.

Published by Packt, this repository houses comprehensive Python examples from the 'Python Machine Learning, 2nd Edition' book. It encompasses crucial machine learning topics including classification, data processing, ensemble learning, and deep learning with TensorFlow. Tailored for both beginners and experienced practitioners, each chapter delivers practical guidance enriched with theoretical understanding. This edition enhances graphical elements, resolves previous inaccuracies, and introduces new sections on managing imbalanced data and deep learning methods.

Discover detailed TensorFlow code examples from Stanford's CS 20 course, specifically designed for deep learning research. This regularly updated repository includes the syllabus, lecture notes, and content from past courses to enhance comprehension of deep learning methodologies. Built for Python 3.6 and TensorFlow 1.4.1, it offers setup guidance and necessary dependencies, serving as a valuable tool for both novice and seasoned AI learners.

FasterTransformer offers highly optimized transformer-based encoders and decoders for GPU-driven inference. Utilizing CUDA and C++, it integrates seamlessly with TensorFlow, PyTorch, and Triton, providing practical examples. Key features include FP16 precision and INT8 quantization for substantial speedup in BERT, decoder, and GPT tasks, enhancing processing efficiency across NVIDIA GPU architectures.

Darkflow utilizes the YOLO framework to deliver efficient real-time object detection and classification. Compatible with Python 3, TensorFlow, and OpenCV, it provides GPU and CPU support. Features include easy installation, flexible model configurations, and JSON output, making it ideal for scalable object detection across applications.

Frigate is a local NVR for Home Assistant that enhances IP camera surveillance using AI object detection. It leverages TensorFlow and OpenCV for efficient real-time object detection. Features such as low-overhead motion detection, multiprocessing for maximum FPS, and video recording ensure resource efficiency. Users benefit from seamless integration through MQTT communication, 24/7 recording, and convenient re-streaming using RTSP. Frigate supports low-latency live views with WebRTC and MSE, simplifying security operations through an advanced interface.

Discover Tacotron, an open-source neural model for converting text to speech using TensorFlow. This project includes audio samples from models trained on datasets like LJ Speech and Nancy Corpus, and features enhancements such as location-sensitive attention. Detailed guides for installation, training, and utilizing pre-trained models are provided, along with monitoring tips using Tensorboard and common troubleshooting advice. It is an essential resource for developers exploring speech synthesis.

DIGITS is a deep learning GPU training system supporting Caffe, Torch, and TensorFlow frameworks. Its web interface simplifies model training, while a platform for feature requests encourages community-driven development. Comprehensive documentation and multi-platform installation guides enhance accessibility, with Docker containers for streamlined deployment. Offering tutorials and advanced examples, DIGITS accommodates both beginners and advanced users. Community engagement is facilitated through forums and user groups, promoting collaborative troubleshooting and feature enhancements.

DeepLabCut is a comprehensive toolkit offering precise, markerless pose estimation for various animal species and behaviors. It employs advanced deep learning techniques to deliver robust performance even with limited training data and video compression. Utilizing a PyTorch backend, it provides easy installation, user flexibility, and enhanced performance. Its capabilities in real-time support and tracking multiple animals expand its utility in fields like neuroscience, ecology, and AI research. The active open-source community ensures its continuous development and support.

PyKoSpacing is a Python package designed for automatic Korean word spacing, leveraging deep learning to enhance text analysis, particularly for SNS and SMS. With an accuracy of over 97% in colloquial Korean text, it ensures precise preprocessing for NLP applications. The tool manages intricate word boundaries with ease and offers straightforward installation and examples for various systems. It supports custom spacing rules and accommodates mixed scripts, making it a versatile resource for text analysis.

KerasTuner offers a user-friendly, scalable solution for hyperparameter optimization in TensorFlow models, supporting Python 3.8+ and TensorFlow 2.0+. With algorithms like Bayesian Optimization, Hyperband, and Random Search, it integrates easily, allowing customization and experimentation. Ideal for enhancing machine learning models.

Explore a hands-on resource for Sklearn and TensorFlow, complete with straightforward instructions for setup via Docker, PYPI, and NPM. This guide is suitable for those looking for practical insights, supporting output formats like HTML, PDF, EPUB, and MOBI. Also includes resources for data analysis in Python from ApacheCN for a comprehensive learning experience.

Discover a set of TensorFlow tutorials created specifically for beginners with clear examples for both TF v1 and v2. Learn traditional and modern practices, such as layers, estimators, and dataset APIs. The project includes instructions on basic operations, linear and logistic regressions, word embedding models, gradient boosting, and various neural network architectures. Additional guides cover data management, multi-GPU training, and customized layers. Keep up-to-date with the latest TensorFlow methods and enhance your skills with practical, hands-on experiences.

TFLearn provides a flexible deep learning library based on TensorFlow, featuring an intuitive high-level API to accelerate experimentation. It offers diverse neural network models, configurable layers, and efficient functions for training. Fully compatible with TensorFlow, TFLearn supports both CPU and GPU configurations. The library enhances transparency with comprehensive graph visualizations and accommodates contemporary models like LSTM and generative networks. The latest version is aligned with TensorFlow v2.0+, ensuring up-to-date deep learning methodologies.

Utilize TensorFlow to apply rapid artistic styles to photos and videos with the fast style transfer. Combining expertise from Gatys, Johnson, and Ulyanov, this tool leverages instance normalization and perceptual loss for real-time effects. It supports diverse artistic styles from famous paintings like 'Udnie'. Detailed resources are available for training and video implementation, offering efficient setup and performance for compatible GPU users.

A Python library built on Keras and TensorFlow, enabling the efficient creation of customizable neural networks for text generation. Supports character and word-level outputs with features like attention-weighting and skip-embedding. Configurable RNN dimensions and bidirectional use enhance training efficiency on GPUs, making it applicable for tasks from chatbots to creative content generation.

AutoKeras, originating from Texas A&M University's DATA Lab, offers a streamlined approach to deep learning with AutoML features. Designed for both beginners and professionals, it provides a user-friendly platform to develop machine learning models with ease. Supporting Python 3.8+ and TensorFlow 2.8+, AutoKeras comes with tutorials and projects to aid learning. Installation through pip enables the application of advanced tools, including image classification. As a community-supported initiative, contributions are encouraged on GitHub. Discover how AutoKeras makes advanced machine learning accessible to all.

This open-source project re-implements Single Shot MultiBox Detector in TensorFlow with a focus on modular VGG-based SSD networks. It supports popular datasets like Pascal VOC, offers tools for easy training and evaluation, and provides options for extending to networks like ResNet. Ideal for computer vision research.

TensorFlow is a leading open-source framework for machine learning, recognized for its comprehensive suite of tools and libraries. Initially developed by Google Brain, it facilitates cutting-edge ML research and application development. With stable Python and C++ APIs, it supports multiple languages and offers diverse installation options, including GPU capabilities. Engage with the TensorFlow community for collaborative advancement in machine learning.

NVIDIA's DALI library improves deep learning workflows by moving data loading and preprocessing tasks from CPU to GPU, thus overcoming CPU bottlenecks. It enhances performance for complex tasks like image classification and object detection. With compatibility across popular frameworks such as TensorFlow, PyTorch, and PaddlePaddle, DALI ensures smooth integration and application portability. It supports a wide range of data formats and offers multi-GPU scalability features, making it suitable for research and production. Additionally, DALI integrates with NVIDIA Triton Inference Server, facilitating efficient deployment of optimized inference models.

Explore a framework tailored for building neural network models, focusing on sequence models within TensorFlow. From automatic speech recognition to advanced machine translation, learn about Lingvo's capabilities for constructing and refining AI models. With support for various TensorFlow versions, Lingvo provides guidance on installation, model execution, and is suitable for both beginners and seasoned developers. Access detailed documentation, community contributions, and a suite of tools designed to enhance your AI research and development.

Ivy enables smooth conversion of machine learning models and libraries between frameworks such as PyTorch, TensorFlow, JAX, and NumPy while maintaining full functionality. Use ivy.transpile for converting code and ivy.trace_graph for crafting graph-based models. Install Ivy via pip and access a variety of demos and documentation. Empower your development workflow by seamlessly converting code and exploring examples ranging from model transpilation to library adaptations for versatile machine learning projects.

Talos facilitates automated hyperparameter tuning and model evaluation in TensorFlow and Keras, aiming for robust results without complexity. It assists researchers and data scientists with a straightforward, single-line pipeline, ensuring complete control over models. With no new syntax to master and minimal additional workload, Talos supports efficient exploration across various prediction tasks. Key features include dynamic optimization strategies, live training monitoring, and detailed analytics, suitable for different OS and hardware configurations. Discover how Talos can optimize model efficiency with simple integration.

The 12-week program offers an introduction to Artificial Intelligence, covering neural networks, deep learning, and practical experience with TensorFlow and PyTorch. It delves into different AI methodologies, including symbolic AI and genetic algorithms, while omitting business uses and deep mathematics. Ethical implications and additional opportunities for learning are available through resources like Microsoft Learn.

DeepSpeech is an open-source speech-to-text engine powered by machine learning, inspired by Baidu's Deep Speech research. It employs TensorFlow, providing comprehensive documentation for installation, usage, and model training at deepspeech.readthedocs.io. Access the latest releases, pre-trained models, and contribution guidelines on GitHub. This project is ideal for developers in search of reliable and scalable speech recognition solutions.

Thinc simplifies deep learning with its lightweight, type-checked API. It seamlessly integrates with PyTorch, TensorFlow, and MXNet, allowing for flexible model composition and configuration. Ideal for use as an interface layer or standalone toolkit, Thinc supports Python 3.6+ and is valued for production reliability across platforms.

Edward is a Python library for probabilistic modeling, inference, and evaluation. It supports various model types such as directed graphical models and neural networks using TensorFlow. The library provides inference methods like variational inference and Monte Carlo techniques, advancing research in Bayesian statistics, machine learning, and probabilistic programming. Edward facilitates model criticism with tools for evaluations and predictive checks. Ideal for experiments from hierarchical to complex deep learning models on extensive datasets.

Discover the extensive code repository of 'Python Machine Learning, 3rd Ed.', a crucial resource for understanding machine learning with Python and TensorFlow. This edition encompasses topics like classification, regression, clustering, neural networks, and reinforcement learning. It offers practical advice on data pre-processing, model evaluation, and hyperparameter optimization, featuring hands-on examples for real-world applications like sentiment analysis and image classification.

Discover DeepMind's Graph Nets library designed for creating graph networks using TensorFlow and Sonnet. Easily accessible through pip, the library is compatible with both CPU and GPU versions of TensorFlow. Graph Nets enable efficient graph neural network development, supporting graph-structured data processing. Interactive Jupyter demos illustrate its application in shortest path, sorting, and physics tasks. This library is well-suited for those interested in harnessing the adaptability and strength of graph networks in complex data modeling.

This comprehensive list highlights popular open-source deep learning projects available on GitHub, including prominent tools such as TensorFlow, PyTorch, and OpenCV. These libraries and frameworks are vital for machine learning professionals, offering solutions tailored to applications in areas like neural networks and computer vision. With easy-to-use interfaces, strong community support, and robust functionalities, they cater to both beginners and advanced users focused on developing and enhancing machine learning skills and solutions.

This repository provides straightforward tutorials on TensorFlow, featuring well-structured code examples for efficient learning. Updated for TensorFlow 2.3, it offers resources for both beginners and experienced users, covering topics from basic machine learning to advanced training techniques. Each tutorial includes source code and documentation for practical learning. Join a growing community and explore additional resources such as a free TensorFlow Roadmap eBook.

Explore a variety of interactive machine learning experiments utilizing Jupyter/Colab notebooks and demo pages. This project encompasses a range of concepts, from supervised learning with TensorFlow and Keras to CNNs and RNNs. Serving as an educational platform, it allows for the practice of different algorithms and datasets. Suitable for experimentation with models such as Multilayer Perceptron for digit recognition and CNNs for image classification. Gain insights into model training and performance through comprehensive demos and sessions. This project is intended for educational exploration rather than optimized deployment.

UER-py is a toolkit for pre-training and fine-tuning NLP models on general-domain corpora and downstream tasks. It features a modular architecture supporting models such as BERT and GPT-2, facilitating the extension and utilization of pre-trained models from its model zoo. Achieving high performance in tasks like classification and reading comprehension, UER-py is compatible with CPU and multi-GPU systems, offering comprehensive functions for researchers to explore and optimize advanced models.

Explore the TensorFlow implementation of Tacotron for end-to-end text-to-speech synthesis using publicly available datasets like LJ Speech. Gain insights into training processes, including hyperparameter adjustment, data downloading, and synthesis. Key features such as attention plot monitoring and gradient clipping offer valuable learning for TTS system advancements.

This content presents a detailed overview of the 'Zero to Mastery Deep Learning with TensorFlow' course, focusing on foundational concepts, neural network construction, and TensorFlow/Keras application. The course features step-by-step coding challenges and project-based learning for enhanced deep learning proficiency. Key components include comprehensive course materials, regular updates, practical exercises, video tutorials, an online book, and a TensorFlow Cheatsheet, catering to users with basic Python and machine learning background.

Foolbox is a Python library for executing adversarial attacks on models built with PyTorch, TensorFlow, and JAX. Utilizing EagerPy, it delivers native performance and supports cutting-edge attacks for robust model testing. With extensive type checking and comprehensive documentation, it facilitates easy integration and contribution to its growth. It requires Python 3.8+ and separate framework installations for tailored flexibility.

Comprehensive TensorFlow tutorials for deep learning beginners, covering from linear models to advanced concepts. Updated for TensorFlow 2, includes YouTube videos and Google Colab support for easy access, serving both theoretical and practical needs.

This comprehensive repository includes 35 Python notebooks focusing on data manipulation, classical machine learning, computer vision, and NLP. It offers practical guidance on MLOps, TensorFlow, and Scikit-Learn, enhanced by hands-on exercises. Topics encompass deep learning architectures, data analysis, visualization, and model deployment. Regular updates include transfer learning methods and advanced neural network techniques, beneficial for data scientists and machine learning engineers.

Discover the basics of Machine Learning with Python using practical examples and interactive notebooks. This project accompanies the first edition of 'Hands-On Machine Learning with Scikit-Learn and TensorFlow,' including exercises and code solutions. The notebooks can be accessed online through Colaboratory, Binder, or Deepnote, and executed locally with an Anaconda setup recommended for Python 3.7. It offers a thorough educational experience for those interested in applying machine learning principles through practical scenarios.

The project delivers structured and clear tutorials with optimized code for TensorFlow, assisting both novices and seasoned developers in mastering complex deep learning tasks. The repository supplies source code and documentation that clarifies model complexities, fostering an expanding community through tutorials from fundamental operations to sophisticated neural networks, all designed to enhance effective TensorFlow use.

Discover in-depth guides and tutorials on TensorFlow designed to improve understanding and practical implementation of this widely-used machine learning framework. Contributors can access resources like CONTRIBUTING.md and the contributor guide for efficient collaboration. Join the TensorFlow community through forums or engage in community translations to help localize content. Learn about issue reporting and become part of a global network contributing to open-source TensorFlow documentation.

Explore advanced deep learning examples optimized for NVIDIA GPUs such as Volta, Turing, and Ampere architectures. Access updated models from the NVIDIA GPU Cloud (NGC) Container Registry for high accuracy and performance. Applications include computer vision and natural language processing, utilizing multi-GPU and multi-node configurations. These models leverage NVIDIA's CUDA-X software stack and Tensor Cores. From EfficientNet to BERT and Jasper, find examples suited for diverse deep learning tasks. Participate in contributions on GitHub and help refine these models.

Netron is a multi-device tool for viewing a broad range of machine learning models, supporting formats like ONNX and TensorFlow Lite, with additional experimental framework compatibility. Installable on major platforms, it simplifies the understanding of complex models for developers, researchers, and data scientists.

The dc_tts project introduces a text-to-speech model that employs deep convolutional networks with guided attention, emphasizing efficient training and quality synthesis. The project examines diverse datasets such as LJ Speech and KSS, incorporating techniques like layer normalization and adaptive learning rates to improve performance. Training scripts are available for users to generate and evaluate synthetic speech, aiming for greater efficiency over Tacotron through exclusive use of convolutional layers.

Python library with 70+ image augmentations for classification and detection, compatible with PyTorch/TensorFlow. Widely adopted in research and competitions for its speed and user-friendly API.

Horovod facilitates distributed deep learning by easing the transition from single-GPU to multi-GPU and multi-host setups. Utilizing MPI and NCCL, it minimizes code changes while boosting training speed and efficiency. Compatible with TensorFlow and PyTorch, Horovod is easy to install and use, backed by the LF AI & Data Foundation for community and documentation support, ideal for those utilizing open-source AI technologies.

Facenet implements face recognition using TensorFlow, inspired by renowned academic works. It supports TensorFlow r1.7 and Python 3.x/2.7 environments, featuring models trained on CASIA-WebFace and VGGFace2, offering high accuracy through MTCNN-aligned data. This project is suited for developing scalable face recognition systems with comprehensive training resources.

QKeras enhances Keras by introducing quantized layer replacements, facilitating efficient transition to quantized networks while preserving Keras’s core strengths of modularity and user-friendliness. It aids in designing low-latency models for edge devices and offers tools to estimate energy consumption. Explore how QKeras streamlines model quantization.