Open Source Deep Learning Frameworks
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Deep Learning Frameworks
View 99 business solutionsBrowse free open source Deep Learning Frameworks and projects below. Use the toggles on the left to filter open source Deep Learning Frameworks by OS, license, language, programming language, and project status.
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OpenCV
Open Source Computer Vision Library
The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials! https://docs.opencv.org/master Books about the OpenCV are described here: https://opencv.org/books.html -
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DeepFaceLab
The leading software for creating deepfakes
DeepFaceLab is currently the world's leading software for creating deepfakes, with over 95% of deepfake videos created with DeepFaceLab. DeepFaceLab is an open-source deepfake system that enables users to swap the faces on images and on video. It offers an imperative and easy-to-use pipeline that even those without a comprehensive understanding of the deep learning framework or model implementation can use; and yet also provides a flexible and loose coupling structure for those who want to strengthen their own pipeline with other features without having to write complicated boilerplate code. DeepFaceLab can achieve results with high fidelity that are indiscernible by mainstream forgery detection approaches. Apart from seamlessly swapping faces, it can also de-age faces, replace the entire head, and even manipulate speech (though this will require some skill in video editing). -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an expert. Students love YOLOv5 for its simplicity and there are many quickstart examples for you to get started within seconds. Export and deploy your YOLOv5 model with just 1 line of code. There are also loads of quickstart guides and tutorials available to get your model where it needs to be. Create state of the art deep learning models with YOLOv5 -
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Netron
Visualizer for neural network, deep learning, machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, Keras, TensorFlow Lite, Caffe, Darknet, Core ML, MNN, MXNet, ncnn, PaddlePaddle, Caffe2, Barracuda, Tengine, TNN, RKNN, MindSpore Lite, and UFF. Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open using the browser version. It is supported by macOS, Windows, Linux, Python Server and browser. -
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PyTorch
Open source machine learning framework
PyTorch is a Python package that offers Tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on tape-based autograd system. This project allows for fast, flexible experimentation and efficient production. PyTorch consists of torch (Tensor library), torch.autograd (tape-based automatic differentiation library), torch.jit (a compilation stack [TorchScript]), torch.nn (neural networks library), torch.multiprocessing (Python multiprocessing), and torch.utils (DataLoader and other utility functions). PyTorch can be used as a replacement for Numpy, or as a deep learning research platform that provides optimum flexibility and speed. -
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Video-subtitle-extractor
A GUI tool for extracting hard-coded subtitle (hardsub) from videos
Video hard subtitle extraction, generate srt file. There is no need to apply for a third-party API, and text recognition can be implemented locally. A deep learning-based video subtitle extraction framework, including subtitle region detection and subtitle content extraction. A GUI tool for extracting hard-coded subtitles (hardsub) from videos and generating srt files. Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu and Ali to complete text recognition locally. Support GPU acceleration, after GPU acceleration, you can get higher accuracy and faster extraction speed. (CLI version) No need for users to manually set the subtitle area, the project automatically detects the subtitle area through the text detection model. Filter the text in the non-subtitle area and remove the watermark (station logo) text. -
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ONNX Runtime
ONNX Runtime: cross-platform, high performance ML inferencing
ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Support for a variety of frameworks, operating systems and hardware platforms. Built-in optimizations that deliver up to 17X faster inferencing and up to 1.4X faster training. -
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OpenVINO
OpenVINO™ Toolkit repository
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
AirSim is an open-source, cross platform simulator for drones, cars and more vehicles, built on Unreal Engine with an experimental Unity release in the works. It supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way. AirSim is fully enabled for multiple vehicles. This capability allows you to create multiple vehicles easily and use APIs to control them. -
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Computer Vision Annotation Tool (CVAT)
Interactive video and image annotation tool for computer vision
Computer Vision Annotation Tool (CVAT) is a free and open source, interactive online tool for annotating videos and images for Computer Vision algorithms. It offers many powerful features, including automatic annotation using deep learning models, interpolation of bounding boxes between key frames, LDAP and more. It is being used by its own professional data annotation team to annotate millions of objects with different properties. The UX and UI were also specially developed by the team for computer vision tasks. CVAT supports several annotation formats. Format selection can be done after clicking on the Upload annotation and Dump annotation buttons. -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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MIT Deep Learning Book
MIT Deep Learning Book in PDF format by Ian Goodfellow
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville. An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville. Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. This is not available as PDF download. So, I have taken the prints of the HTML content and bound them into a flawless PDF version of the book, as suggested by the website itself. Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well. -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms. TensorRT is built on CUDA®, NVIDIA’s parallel programming model, and enables you to optimize inference leveraging libraries, development tools, and technologies in CUDA-X™ for artificial intelligence, autonomous machines, high-performance computing, and graphics. With new NVIDIA Ampere Architecture GPUs, TensorRT also leverages sparse tensor cores providing an additional performance boost. -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides native Java development experience and functions like any other regular Java library. You don't have to be a machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. -
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Keras
Python-based neural networks API
Python Deep Learning library -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning. -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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HanLP
Han Language Processing
HanLP is a multilingual Natural Language Processing (NLP) library composed of a series of models and algorithms. Built on TensorFlow 2.0, it was designed to advance state-of-the-art deep learning techniques and popularize the application of natural language processing in both academia and industry. HanLP is capable of lexical analysis (Chinese word segmentation, part-of-speech tagging, named entity recognition), syntax analysis, text classification, and sentiment analysis. It comes with pretrained models for numerous languages including Chinese and English. It offers efficient performance, clear structure and customizable features, with plenty more amazing features to look forward to on the roadmap. -
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TTS
Deep learning for text to speech
TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for measuring dataset quality, and is already used in 20+ languages for products and research projects. Released models in PyTorch, Tensorflow and TFLite. Tools to curate Text2Speech datasets underdataset_analysis. Demo server for model testing. Notebooks for extensive model benchmarking. Modular (but not too much) code base enabling easy testing for new ideas. Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). Speaker Encoder to compute speaker embeddings efficiently. Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN). If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option. -
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Unity ML-Agents Toolkit
Unity machine learning agents toolkit
Train and embed intelligent agents by leveraging state-of-the-art deep learning technology. Creating responsive and intelligent virtual players and non-playable game characters is hard. Especially when the game is complex. To create intelligent behaviors, developers have had to resort to writing tons of code or using highly specialized tools. With Unity Machine Learning Agents (ML-Agents), you are no longer “coding” emergent behaviors, but rather teaching intelligent agents to “learn” through a combination of deep reinforcement learning and imitation learning. Using ML-Agents allows developers to create more compelling gameplay and an enhanced game experience. Advancement of artificial intelligence (AI) research depends on figuring out tough problems in existing environments using current benchmarks for training AI models. Using Unity and the ML-Agents toolkit, you can create AI environments that are physically, visually, and cognitively rich. -
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Coqui STT
The deep learning toolkit for speech-to-text
Coqui STT is a fast, open-source, multi-platform, deep-learning toolkit for training and deploying speech-to-text models. Coqui STT is battle-tested in both production and research. Multiple possible transcripts, each with an associated confidence score. Experience the immediacy of script-to-performance. With Coqui text-to-speech, production times go from months to minutes. With Coqui, the post is a pleasure. Effortlessly clone the voices of your talent and have the clone handle the problems in post. With Coqui, dubbing is a delight. Effortlessly clone the voice of your talent into another language and let the clone do the dub. With text-to-speech, experience the immediacy of script-to-performance. Cast from a wide selection of high-quality, directable, emotive voices or clone a voice to suit your needs. With Coqui text-to-speech, production times go from months to minutes. -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Offers sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist. -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. Currently, Task-TS from CoronaWhy primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open-source benchmarks and codes for flash flood and river flow forecasting. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference.
Guide to Open Source Deep Learning Frameworks
Open source deep learning frameworks are programming libraries that enable developers to build and train AI models for a variety of tasks, including computer vision, natural language processing, and robotics. Unlike traditional software development tools, open source deep learning libraries have extensible architecture that is designed to make the process of constructing complex models simpler and more efficient.
Deep learning frameworks can be categorized into three main types: low-level libraries such as TensorFlow or PyTorch; high-level image recognition libraries like OpenCV; and end-to-end systems like Caffe or Keras. Low-level deep learning frameworks provide basic building blocks for building AI programs—neural networks, loss functions, optimization algorithms, etc.—while high-level ones focus on creating production ready model architectures with minimal effort. End-to-end systems are geared towards tasks where users can simply plug in data to get an output without worrying about the complexity of underlying machine learning algorithms.
One advantage that open source deep learning frameworks offer over proprietary solutions is cost savings since they’re free to use. Additionally these frameworks are typically highly customizable due to their modularized design philosophy which enables developers to mix and match components for optimal performance depending on the application at hand. Furthermore there’s usually a large community of users who regularly share tips & tricks and contribute code back to the project, enabling easier bug fixes & feature additions from experienced programmers all around the world. Finally due to their popularity open source deep learning platforms often contain popular features & improvements not found in commercial products which makes them ideal candidates for enterprise level applications or research projects alike.
What Features Do Open Source Deep Learning Frameworks Provide?
- Graph Convenience: Open source deep learning frameworks provide a great way to easily visualize different neural network architectures and how they are connected. This makes it easy to visualize the data flow of inputs and outputs, as well as seeing exactly how the nodes and layers interact with each other.
- Automation: One of the major advantages to using an open source deep learning framework is that it automates many of the tedious tasks associated with training a model. Frameworks such as Pytorch and TensorFlow provide powerful libraries that can be used to quickly implement various models without having to manually implement them yourself.
- Support for Popular Machine Learning Libraries: Most popular open source deep learning frameworks come pre-packaged with a variety of libraries that make it easier for developers to create complex models faster, including support for popular machine learning packages like scikit-learn. This eliminates the need for developers to use external packages or tools in order to craft their desired model structure.
- Easy Model Sharing: Deep learning frameworks also allow for users to share their trained models with others easily due to their standardized format and architecture design across different machines. This makes deploying models created on one system on another system much simpler than if you were relying entirely on custom software implementations.
- Flexibility With Architecture Design: By utilizing an established framework, users can take advantage of greater flexibility when designing new architectures compared to if they had invested in proprietary solutions from companies like Google or Microsoft Azure which often have limited configuration options available during initial setup. Additionally, they enjoy added scalability features that let them adjust performance based on specific requirements such as environment size or computation power availability at run time levels; thus allowing them more control over deployment processes than ever before possible.
What Are the Different Types of Open Source Deep Learning Frameworks?
- TensorFlow: Developed by Google, this open source deep learning framework is based on dataflow graphs and is used for numerical computation. It includes both high-level and low-level APIs to construct various neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- PyTorch: This open source deep learning framework was developed by Facebook's AI Research team and offers a seamless experience of building dynamic computational graphs for machine learning applications. It provides a comprehensive set of tools necessary for building deep learning models at scale.
- Caffe: A popular open source deep learning framework, Caffe is written in C++ and CUDA with an expressive architecture that enables researchers to effectively express their ideas without sacrificing performance. It can be used not only as a regular deep learning library but also as a reference implementation for research papers.
- MXnet: An Apache-licensed, portable, flexible distributed/parallel training and inference engine, MXNet allows easy operations on existing hardware or cloud platforms. Its programming language support includes R, Python, Julia, GoLang, Perl etc., allowing developers to build end-to-end solutions using the same platform.
- Theano: Originated from the University of Montreal’s MILA lab in 2007, Theano provides robustness through strong static typing capabilities while enabling users to access sophisticated mathematical libraries such as NumPy efficiently during programming workflows of deep learning projects.
- Keras: This open source library supports developing new models quickly while running seamlessly on either the CPU or GPU platforms with its abstractions inspired by minimalistic user interface design principles. Keras offer adaptive optimization algorithms that enable rapid experimentation along with deployment support for any major operating system including Windows 10 and iOS devices.
What Are the Benefits Provided by Open Source Deep Learning Frameworks?
- Flexibility: Open source deep learning frameworks provide users with the ability to modify code, making it easier to customize models. This flexibility allows for more effective use of resources and a greater degree of control over the project, allowing for faster development times.
- Collaboration: With open source deep learning frameworks, developers have access to a wide range of resources that can be combined and reused in different ways. This enables collaboration between teams from all over the world, increasing the potential size and reach of projects.
- Cost Savings: Building upon existing code provided by an open source deep learning framework reduces development costs significantly since no software licenses are required and any necessary hardware can generally be purchased cheaper than commercial equivalents.
- Security: Since open source deep learning frameworks utilize publicly available libraries, they are subject to regular security checks by independent organizations that help ensure their reliability. Additionally, using an open source platform can reduce the risks associated with proprietary systems because it is transparent and accessible to anyone who wants to review or make changes as needed.
- Portability: Open source deep learning frameworks are designed to run on multiple platforms including Windows, Mac OS X, Linux, etc., making them extremely versatile in terms of deployment options. Additionally, these platforms often include tools that allow models to be deployed across devices such as mobile phones or embedded systems with ease.
Who Uses Open Source Deep Learning Frameworks?
- Data Scientists: Data scientists use open source deep learning frameworks to create powerful and accurate models that can interpret and extract useful insights from huge amounts of data.
- Researchers: Researchers rely on open source deep learning frameworks to develop new algorithms and techniques for artificial intelligence projects.
- Students: Students use open source deep learning frameworks to gain hands-on experience with complex technologies in a controlled environment.
- Developers: Developers employ open source deep learning frameworks to create practical applications such as natural language processing, machine translation, computer vision, recommendation systems and more.
- Hobbyists: Hobbyists may use open source deep learning frameworks to explore the potential of artificial intelligence or build simple home hobby projects.
- Businesses: Businesses utilize open source deep learning frameworks to increase their efficiency by streamlining tasks that were once done manually by humans. This enables businesses to cut costs while using less resources at the same time.
How Much Do Open Source Deep Learning Frameworks Cost?
Open source deep learning frameworks offer a great option for those looking to get into machine learning and deep learning without spending any money. While the actual "cost" of using open source deep learning frameworks is nothing, it does require an investment of time and energy in order to learn how to use them. Depending on the specific framework, you might need to invest several weeks or months in order to become familiar with the fundamentals and understand how to apply it for various tasks. Additionally, many open source frameworks require additional libraries, packages, or hardware (like GPUs) depending on what type of project you're doing.
Overall, even though there isn't any actual cost associated with open source deep learning frameworks, it's important that users be aware that there may be some investments required in terms of time and resources before they can start taking advantage of this incredible resource.
What Software Can Integrate With Open Source Deep Learning Frameworks?
Open source deep learning frameworks can integrate with a wide variety of software types, including software for data processing, machine learning algorithms and development, devOps automation and deployment, and more. Data processing software such as Pandas can help pre-process large datasets to prepare them for training in an open source framework. Machine learning libraries like Scikit-Learn or PyTorch provide tools to explore, visualize and build models. DevOps automation tools help move trained models from development into production environments in order to deploy the model at scale and handle routine monitoring tasks. Finally, other software like Jupyter Notebook or TensorBoard can be used to aid in debugging or visualizing the results of training runs. Open source deep learning frameworks offer immense flexibility when it comes to connecting up with different types of software necessary to deliver full rollouts of powerful AI solutions.
Recent Trends Related to Open Source Deep Learning Frameworks
- TensorFlow: TensorFlow is an open source deep learning tool created by Google. It has become the most popular deep learning framework in recent years due to its easy-to-use API and wide range of features. It is used for training models and deploying them for inference, making it popular for applications such as natural language processing, image recognition, and more.
- PyTorch: PyTorch is a machine learning library developed by Facebook which has quickly gained popularity due to its flexibility and ease-of-use. It has a unified API for both training and inference making it popular for tasks such as computer vision, natural language processing, and reinforcement learning.
- Caffe: Caffe is an open source deep learning framework created by the Berkeley AI Research (BAIR) lab at UC Berkeley. It is focused on speed and simplicity, making it popular for tasks such as image classification, segmentation, face recognition, and object detection.
- MXNet: MXNet is an open source deep learning framework developed by Apache Software Foundation. It can be used on multiple devices such as CPUs, GPUs, mobile phones, etc., making it popular for applications such as image recognition or video streaming.
- Theano: Theano is an open source deep learning library developed by University of Montreal’s Yoshua Bengio et al in 2007. It has been popular due to its efficient algorithms for high dimensional data including matrix multiplication and convolutional neural networks (CNN). Theano can be used for tasks such as natural language processing, recommendation systems and more.
- Keras: Keras is a high-level neural networks API written in Python that was developed by François Chollet et al in 2015. It runs on top of either TensorFlow or Theano, making it popular for tasks such as image recognition or text summarization.
How To Get Started With Open Source Deep Learning Frameworks
Getting started with open source deep learning frameworks is a great way to become comfortable with developing and deploying deep learning experiments. With the ever-increasing amount of data available, more individuals are leveraging deep learning technology to build powerful models faster than ever before. The most popular open source frameworks for developing neural networks are TensorFlow, Pytorch and Caffe.
TensorFlow is one of the most widely used open source frameworks and provides dynamic computational graphs with efficient memory management capabilities that make it easy to deploy models on multiple platforms including CPUs, GPUs, distributed systems and mobile devices. It also includes high level APIs like Keras that allow users to quickly set up training pipelines without getting bogged down in manual coding or model-building steps.
Pytorch is another popular framework that offers an intuitive Python-based programming interface for creating complex neural networks in a just few lines of code. It also supports dynamic computation graphs which make it easier to debug a network’s architecture during development, helps developers easily scale up their models using GPUs and take advantage of other hardware optimizations such as NVIDIA's TensorRT integration for faster inference times during deployment.
Caffe is a fast growing open source framework developed by Berkeley AI Research Lab (BAIR). It has been designed to be user-friendly while offering highly optimized features such as efficient storage formats across both mobile devices and server deployments, auto-mixed precision support, and constrained optimization methods for parameter tuning among others. For users who have limited computer vision experience but would like to get started quickly on image recognition tasks - Caffe can be an ideal choice since its trained models – CNNs – can be deployed on many platforms (e.g., iOS/Android) with minimal effort required from the user doing the integration work.
To get started with any of these open source deep learning frameworks users will first need some basic understanding of the mathematics behind neural networks such as linear algebra basics, calculus derivatives & gradient descent etcetera; Next step involves downloading the specific software package — for example installing Anaconda distribution (https://www.anaconda.com/) conveniently bundles together all the libraries needed for data science projects built using Python language; Then comes step two: once you have set up your own environment by installing necessary software packages start reading relevant books/tutorials or watch online videos about general topic areas such as convolutional neural networks(CNNs), recurrent neural networks(RNNs), long short term memory units(LSTMs) etcetera; Step three must involve setting up some real-world project where you apply what you learnt so far.