#deep learning

The framework is optimized for mobile platforms, providing high-performance neural network inference without third-party dependencies. It achieves cross-platform compatibility, outperforming other open-source frameworks on mobile CPUs. Developers can efficiently deploy deep learning algorithms on mobile devices, enabling the development of intelligent apps that integrate AI into everyday use. It is utilized in various Tencent apps, such as QQ and WeChat.

NNI is a robust toolkit that automates various aspects of deep learning, including feature engineering and model optimization processes like neural architecture search and hyperparameter tuning. Its documentation offers the latest updates while ensuring compatibility with frameworks like PyTorch and TensorFlow. NNI efficiently implements algorithms ranging from exhaustive searches to Bayesian optimizations. Its adaptability across local setups and cloud services ensures scalability without integration hassles. Extensive tutorials and community support further assist in enhancing project potential.

Discover extensive deep learning resources featuring expert-led video lectures, comprehensive notes, and practical datasets. Perfect for both beginners and advanced learners aiming to enhance AI skills and employability, including in top firms like United Imaging. Join collaborative groups for valuable insights in AI applications.

Discover a comprehensive course focusing on PyTorch for deep learning, including the latest PyTorch 2.0 tutorial. This hands-on course emphasizes practical coding with sections covering neural network classification, computer vision, transfer learning, custom datasets, and model deployment. Through milestone projects such as FoodVision, gain practical experience and develop a portfolio. This beginner-friendly course uses Google Colab notebooks and video content for understanding deep learning fundamentals.

PaddleNLP, built on the PaddlePaddle framework, offers a robust toolkit for large language model development, enabling efficient training, seamless compression, and high-speed inference across diverse hardware platforms including NVIDIA GPUs and Kunlun XPUs. Designed for industrial-grade applications, it facilitates smooth hardware transitions and reduces development costs with advanced pre-training and fine-tuning strategies. The project’s operator fusion strategies enhance parallel inference speed, applicable in fields like intelligent assistance and content creation.

The fastai library offers high-level and low-level components to support both standard deep learning tasks and innovative model customization. Its architecture takes advantage of Python and PyTorch, ensuring usability and performance. Key features include a GPU-optimized vision library, dynamic callback system, and adaptable data block API. Suitable for various deployment needs, fastai also facilitates integration with existing libraries and efficient model training.

ONNX is a community-driven, open-source project that provides a standardized model format to support interoperability across various AI frameworks and tools. It addresses the needs of both deep learning and traditional machine learning, facilitating smooth framework transitions and aiding swift research-to-production deployment. By engaging a wide range of frameworks, tools, and hardware, ONNX enhances AI research and development. The platform invites community collaboration to continually refine its offering, fostering ongoing improvements to support the dynamic field of AI innovation efficiently.

Einops offers a consistent API for tensor operations across platforms such as numpy, pytorch, and tensorflow, focusing on operations like rearrange, reduce, and repeat. Recent enhancements include framework support, functionality improvements, and features such as EinMix for linear layers. Its semantic clarity and uniform behavior facilitate intuitive tensor manipulation, independent of the framework.

This project provides an extensive open-source resource focused on deep learning, combining theory with practical coding applications. It supports the translation of complex formulas into functional code, promoting experiential learning and collaboration within the community. Accessible online without cost, it ensures users remain informed on the rapidly advancing topics in deep learning. The platform includes forums, encouraging robust discussions for both individual learners and educational institutions, receiving notable support from academia and industry professionals.

Explore a curated collection of large language model concepts and interview questions, particularly suited for resource-constrained scenarios. Discover 'tiny-llm-zh', a compact Chinese language model, alongside projects including llama and RAG systems for practical AI learning. Engage with resources on deep learning, machine learning, and recommendation systems.

tsai is a robust open-source deep learning library designed for time series and sequence tasks such as classification, regression, and forecasting. It leverages Pytorch and fastai to integrate innovative models like PatchTST and RNN with Attention. With expanded datasets and Pytorch 2.0 support, tsai offers utilities like walk-forward cross-validation and memory optimization, continually evolving to improve predictive precision. It is well-documented with extensive tutorials, making it a reliable tool for efficient time series data analysis.

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.

This project provides a detailed, step-by-step guide for building large language models (LLMs) from scratch. Focused on practical implementation and theoretical understanding, it includes tutorials and code examples for comprehension and creation of models like ChatGPT. Targeted at those interested in natural language processing and AI, the project emphasizes hands-on learning of LLM architecture, pre-training, and fine-tuning. Participants can explore models such as ChatGLM, Llama, and RWKV, enhancing their understanding of various model functionalities and mechanisms.

Access extensive deep learning documentation with PyTorch Chinese translations, offering optimized use for both GPU and CPU. Engage with the community to explore translated PyTorch 2.0 resources, and visit official sites for continuous updates. This initiative promotes the open dissemination of knowledge and encourages collaborative participation to enhance resource quality. Join the conversation through various platforms for feedback and contributions.

This framework facilitates AI model workflows by providing streamlined pretraining, finetuning, and deployment for flexible and scalable use, especially among PyTorch users. It integrates smoothly with LitServe for model serving, ensuring efficient workflow management. Designed with practicality in mind, it effortlessly handles classification, segmentation, and summarization tasks. Supported by comprehensive documentation and community-driven examples, it serves as a vital resource for various deep learning projects.

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.

Torchreid is an advanced library designed for deep-learning person re-identification, supporting multi-GPU use and both image and video analysis. It facilitates complete training, cross-dataset evaluation, and straightforward dataset setup while implementing leading-edge models. The library is highly adaptable, permitting easy integration of new models, datasets, and techniques, and includes pre-trained models and innovative training methods. Torchreid additionally offers support for ONNX, OpenVINO, and TFLite for model exportation, enhancing adaptability across diverse platforms. It serves as a valuable tool for researchers and developers focused on efficient person re-identification solutions.

Explore a detailed guide on converting design mockups to HTML/CSS with deep learning. From 'Hello World' to advanced models, learn about the Bootstrap version's 97% accuracy with domain-specific tokens and GRU layers. Discover insights from pix2code, Airbnb sketches, and Harvard's im2markup, suited for integrating AI into design workflows on platforms like FloydHub and locally through Jupyter Notebook.

Discover a wide array of resources covering machine learning, Golang, PostgreSQL, and distributed systems, organized into detailed chapters for comprehensive understanding. The project embraces collaboration and accuracy by welcoming pull requests for content improvement. By interacting with translated materials, learners can effectively enhance their programming and database skills. This platform integrates educational content with a community feedback loop, forming an essential hub for technology learners and professionals.

Discover the First Order Motion Model, a deep learning project focused on animating images by transferring motion from driving videos. This repository features example animations across datasets such as VoxCeleb and Fashion, along with step-by-step guides for installation, training, and evaluation. It includes tools for video editing and animation, with applications in face-swap and creative media.

Chainer, a Python-centric deep learning framework, utilizes a define-by-run approach for dynamic computational graphs and automatic differentiation. It supplies high-level APIs for neural network construction and leverages CuPy for superior CUDA-based training and inference. Despite transitioning to a maintenance phase, Chainer remains a robust solution for various deep learning applications, with Docker images ensuring easy deployment and NVIDIA Docker support.

Neurojs, a JavaScript framework for deep learning in the browser, excels in reinforcement learning with illustrative demos like a 2D self-driving car. Though now eclipsed by newer frameworks like TensorFlow-JS, it offers a robust, full-stack neural-network architecture with advanced features such as priority replay buffers and deep-Q networks. Perfect for hands-on experimentation with neural networks directly in the browser.

DeepChem provides an open-source toolchain for applying deep learning in areas such as drug discovery, materials science, quantum chemistry, and biology. It offers integration with Python environments and support for TensorFlow, PyTorch, and JAX, enhancing research efficiency. Rich tutorials, easy installation via pip or conda, and active community involvement make DeepChem a valuable resource for molecular machine learning and computational biology. Setting up with Docker and connecting with the community through Discord, DeepChem supports innovation and scientific progress.

Discover a vast collection of Jupyter notebooks and AI/ML demos tailored for enterprise needs. Expedite readiness assessment, model prototyping, and solution evaluation across marketing, pricing, supply chain management, and manufacturing sectors. Utilize proven methods in deep learning, reinforcement learning, and causal inference to advance decision-making and automation.

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.

Keras 3 is an adaptable deep learning framework compatible with JAX, TensorFlow, and PyTorch. It enhances model building across fields like computer vision and NLP with superior flexibility and performance. This framework ensures effortless scaling from personal setups to datacenter environments. Users benefit from easy installation via pip and improved GPU performance through CUDA integration. Keras 3 supports existing tf.keras code and extends custom components, leveraging each backend's distinct capabilities for robust machine learning solutions.

Discover an extensive collection of tools, libraries, and resources for Natural Language Processing (NLP), covering research summaries, trends, leading research labs, tutorials, and programming libraries. This guide provides resources for multiple languages, services, and annotation tools, supporting the advancement of NLP projects. Suitable for new learners and experienced researchers, it offers insights into deep learning techniques, theories, and practical implementations. Stay informed with the latest progress and enhance understanding through books, courses, and informative blogs.

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.

MONAI, built on PyTorch, is an open-source framework designed for deep learning in medical imaging. It offers state-of-the-art workflows with flexible pre-processing, compositional APIs, and domain-specific tools. Supporting multi-GPU environments, MONAI promotes collaboration among researchers. Its Model Zoo and extensive documentation streamline integration and use, making MONAI a valuable resource in healthcare.

This open-source guide provides a comprehensive introduction to PyTorch based on version 1.8, including basic usage, advanced extensions, and practical applications. It offers a hands-on approach with Jupyter Notebooks, covering topics like vectorization and distributed computing, and guides on projects such as GANs, NLP with Transformers, and Style Transfer. Accessible for those without the accompanying book, this resource includes full code and pre-trained models.

OneFlow is a versatile deep learning framework offering a PyTorch-like API for ease of programming and scalability in n-dimensional-parallel execution. Features such as the Global Tensor and Graph Compiler facilitate efficient model deployment. It supports Linux and Python versions 3.7 to 3.11 and is compatible with CUDA architectures 60 and above. Easily installable via Docker or pip, OneFlow adapts to diverse system configurations. Explore its capabilities in distributed learning with detailed documentation and a supportive community.

Explore Magika, an innovative AI tool for accurate file type detection using deep learning. Achieving over 99% precision across 200+ content types, Magika ensures fast detection on single CPUs. New updates include a Rust CLI and enhanced Python API. The tool enhances Google services like Gmail and Drive, providing support for diverse file formats. Accessible as both command line tools and a web demo, Magika continues to push the boundaries of content identification in the digital landscape.

The project converts the MXNet code of 'Dive into Deep Learning' to TensorFlow2, specifically addressing the Chinese version. Endorsed by original authorship and incorporating PyTorch adaptation insights, it targets those keen on deep learning via TensorFlow2, demanding only fundamental math and coding skills. Accessible via web and local server through docsify, it invites contributions for ongoing enhancement, recognized as an educational asset by significant AI platforms.

MACE is a deep learning inference framework tailored for mobile and heterogeneous computing across Android, iOS, Linux, and Windows. Optimized for performance, it integrates NEON, OpenCL, and the Winograd algorithm for efficient convolution, while advanced APIs like big.LITTLE ensure low power consumption. MACE improves responsiveness with OpenCL kernel management, supports memory optimization and model protection, and is compatible with TensorFlow, Caffe, and ONNX formats. It offers broad compatibility with Qualcomm and MediaTek chips, making it a reliable choice for developers aiming to enhance mobile AI capabilities.

The project delivers a C++14 library designed for deep learning on IoT devices and embedded systems, excelling in environments with constrained resources. It achieves high-speed performance without GPU dependence, supports effortless integration, and accommodates various neural network architectures and activation functions. Noteworthy features include TBB or OpenMP support for parallel computation, Intel SSE/AVX enhancements, and straightforward Caffe model importation. Being header-only, the library ensures extensive portability and serves as an effective tool for learning neural networks.

Deep Java Library (DJL) is a high-level, open-source Java framework that facilitates deep learning integration without requiring machine learning expertise. It supports Java developers to seamlessly incorporate deep learning into applications using familiar Java IDEs. DJL's engine-agnostic feature offers flexibility in computational engine choice, and its ergonomic APIs promote best practices in model training and inference. It also provides automatic hardware optimization and extensive documentation for enhanced development and deployment.

Caffe2 is an advanced deep learning framework, originally based on Caffe, now part of PyTorch for better scalability and adaptability.

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.

MatchZoo is a comprehensive toolkit designed for the development, comparison, and dissemination of deep learning models in text matching. Its robust codebase supports a range of applications including document retrieval, question answering, conversational response ranking, and paraphrase identification. Features like streamlined model configuration, automatic hyper-parameter tuning, and unified data processing enhance its user-friendliness and adaptability. MatchZoo is compatible with Keras and TensorFlow and includes support for models such as DSSM and DRMM. The toolkit is easily installable via PyPI or source, with extensive documentation available for quick setup.

Discover a modular and user-friendly deep learning package designed for CTR prediction. Compatible with TensorFlow 1.x and 2.x, it supports quick testing and large-scale distributed training across various models like DeepFM and xDeepFM, ideal for enhancing predictive analytics in advertising.

This deep learning model converts hand-drawn sketches into HTML code. It utilizes an image captioning approach and builds on projects like pix2code and Design Mockups. As a proof-of-concept, it currently works best with wireframes similar to its main dataset, showing potential in automating web design.

Designed for those with some experience in machine learning, including familiarity with frameworks like Keras, TensorFlow, or Pytorch, this guide makes Pytorch learning accessible with its optimized, easy-to-follow examples and step-by-step progression. Spend 30 minutes to 2 hours daily over 20 days to effectively incorporate Pytorch into real-world projects. The guide serves as a reliable reference, packed with practical examples, for enhancing application development expertise.

Explore a vast repository of papers focused on deep learning methods for object detection. This continually updated resource includes the latest research from major conferences like CVPR, ICCV, and NeurIPS, offering perspectives on the development of deep learning object detection. Review detailed performance tables and identify key articles for researchers and enthusiasts. Learn about significant breakthroughs such as R-CNN, YOLO, and Faster R-CNN for the latest advances in object detection.

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.

GluonCV offers a comprehensive set of state-of-the-art deep learning models for a variety of computer vision tasks such as image classification and object detection. It facilitates quick prototyping for engineers, researchers, and students by supporting both PyTorch and MXNet frameworks. Key features include scripts for reproducing research results, an extensive collection of pre-trained models, and user-friendly APIs to ease implementation. Ideal for both research and production, GluonCV is well-supported by the community and integrates seamlessly with AutoGluon for enhanced model deployment.

Discover Intel's neon, a deep learning framework no longer maintained but offering multi-hardware efficiency. Supporting convolution, RNN, and LSTM layers, it provides out-of-the-box models for immediate application. Known for its rapid iteration capabilities, it outperforms many in speed. Seamless transitioning across CPU, GPU, and Nervana hardware is supported. With Intel discontinuing support, the project invites continued community engagement through forking and maintaining.

Alpha Zero General provides an adaptable implementation of self-play reinforcement learning based on the AlphaGo Zero model. It is compatible with any two-player turn-based game and supports various deep learning frameworks, making it a useful asset for developers. The project includes examples for games like Othello, GoBang, and TicTacToe using PyTorch and Keras. Its design allows for easy customization through subclassing game and neural network templates. Key features include a training loop, Monte Carlo Tree Search, and flexible neural network parameter settings. Setup can be streamlined with nvidia-docker for a Jupyter environment.

Microsoft Cognitive Toolkit (CNTK), an open-source deep learning framework, models neural networks as computational graphs for seamless execution of architectures like DNNs, CNNs, and RNNs. It incorporates stochastic gradient descent with automatic differentiation, supporting multi-GPU and server parallelization, suitable for intensive deep learning applications. Though major updates have halted, CNTK maintains compatibility with the latest ONNX standards, promoting AI framework interoperability. Extensive resources are available for users to explore and optimize the toolkit’s features.

Apache TVM, a robust open-source deep learning compiler stack, enhances the synergy between productivity-centric frameworks and performance-focused hardware. It delivers comprehensive end-to-end compilation for diverse platforms, optimizing deep learning efficiency. Licensed under Apache-2.0, TVM fosters community-based advancement and follows the Apache committer model. Extensive resources for installation, tutorials, and examples are available to assist users. Inspired by projects such as Halide, Loopy, and Theano, TVM creates a cohesive environment to facilitate integration and performance improvements for developers and hardware vendors alike.

The Multi-Tacotron Voice Cloning project is a multilingual phonemic implementation for Russian and English, built on a deep learning framework. The project, an extension of Real-Time-Voice-Cloning, facilitates the creation of numeric voice representations from brief audio samples. It includes pre-trained models and necessary datasets, providing efficient pathways for text-to-speech conversion. The diverse datasets and neural networks such as Tacotron 2 and WaveRNN enable seamless multilingual capabilities, suited for advanced TTS synthesis requirements.