With the fast development of AI technology, more and more people have begun to pay attention to deep learning. Deep learning is a revolutionary approach to artificial intelligence that has made it possible for computers to learn from experience and understand the world in ways that were not previously possible. It has transformed the way we use our computers, and it is quickly making its way into just about every facet of our lives. It's hard to imagine what the world would be like without deep learning, but it's even harder to imagine what it would be like in 2022. This blog post will look at the most popular deep learning frameworks that can help you choose or start your deep learning journey!
Let’s dive in!
Tensorflow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library used to implement deep learning models that run on a CPU or a GPU. It is free software that can be downloaded and used in commercial products but not modified. TensorFlow was originally developed by researchers working on Google's AI team to use as a machine learning platform. Later it was open-sourced as a general-purpose machine learning tool available as an API and can be implemented into other coding languages such as Python or Go.
Ever since it was released, TensorFlow has become the most popular deep learning framework. TensorFlow's flexible architecture allows you to build custom deep learning models and use its components to develop new machine-learning tools. It is pretty much the only single tool you need for almost everything involved in building AI. Generally speaking, if computational requirements are met (GPUs mostly), then TF is the way to go. Today, TensorFlow is used by major companies worldwide, ranging from small startups to Fortune 500 companies.
PyTorch is a popular deep learning framework to build neural networks. It is based on Torch, a scientific computing framework with wide support for machine learning algorithms. PyTorch replaces the underlying engine of Torch with a Python-based, GPU-accelerated dynamic translator. It is open-source software released under the Apache 2.0 license. PyTorch is mainly used for deep learning models, including sequence models, reinforcement learning models, and relational models. It is written in Python, and its API is similar to other deep learning frameworks like Tensorflow and Caffe. It is the best option if you are looking for one framework for all your tasks.
While TensorFlow was the go-to library for Machine Learning, PyTorch is now the preferred choice due to its flexibility and ease of use. While it is easy to use, it can be a bit tricky to learn, especially for beginners. However, PyTorch is strongly backed by great documentation and community! The framework is used by many large companies, including Facebook, Uber, Twitter, Snap, etc. It is written in C++ and Python and supports CUDA and OpenCL. It is available for Linux, Mac OS X, and Windows.
DeepMind's Sonnet, or "Neural Talk," is an open-source neural network framework that lets you rapidly train large deep neural networks. Sonnet is based on TensorFlow, Google's open-source machine learning framework. It allows you to define and train neural networks in a human-readable way and express the learning process with simple Python code. It is easy to set up and use and doesn't need any expert knowledge of Theano or TensorFlow. It's the perfect framework for deep learning researchers looking to develop, test, and deploy machine learning models but may not have the necessary experience to do so. The framework is available on GitHub under Apache 2.0.
Cognitive Toolkit (CNTK) is an open-source unified deep-learning toolkit. It supports a variety of deep learning algorithms and is highly optimized for parallel, distributed, and multi-GPU training. The framework is an implementation of the newer, more efficient neural network architecture. With CNTK, you can build and train deep neural networks that run on CPUs and GPUs. The framework can handle a variety of different types of neural networks and comes with other pre-trained models. Commercial organizations widely use it in computer vision, speech recognition, natural language processing, and predictive analytics. The framework is also used by people working on projects, such as Open AI and the Human Brain Project.
This framework is developed by Microsoft Research, Microsoft AI and others, and is part of Microsoft's Azure AI platform. Cognitive Toolkit is used in the 2016 winning AI system in the annual RoboSub competition (submarine object detection), the clinical decision support system in the IBM Watson Health solution for oncology, and Continental's automated driving solution, which won the 2018 DARPA Grand Challenge.
Caffe2 is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. It is based on the original Caffe framework and supports most of the original's functionality. In addition to the original Caffe's convolutional neural network (CNN) and long short-term memory (LSTM) components, Caffe2 adds a fully connected neural network (FCN) module, a natural language processing (NLP) toolkit, and a VGG-like network that works with image, text, and video data. In contrast with other frameworks, Caffe is a lightweight, modular, and scalable deep learning framework that provides ease of use for rapid experimentation. Caffe2 is applicable to various deep learning scenarios, including image recognition, video analysis, speech recognition, natural language processing, and information retrieval.
Right now, the Caffe2 project joined forces with PyTorch and has completely moved to the PyTorch project.
Gluon is a high-level machine learning library built on MXNet, offering a concise and straightforward API to develop and evaluate deep learning models. This framework is designed to make creating, training, and deploying deep learning models easy and intuitive and removes the low-level imperative programming from the application developer. It supports state-of-the-art models such as the Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) for sequence recognition and is easily extensible. Gluon is written in Java and has models that run on the CPU or the GPU. It provides a unified interface, making it easier for data scientists to switch between frameworks. The Gluon API has the same signature across different frameworks, making it easier to learn and use. It can be freely used in commercial applications, and applications using Gluon are released under the Apache 2.0 license.
Theano is a Python library that can perform mathematical operations and build and train deep learning models. It was initially developed by the University of Montreal and released as an open-source software library in 2008. It can be used for both computational optimizations and for building neural networks. Theano is built on top of NumPy and is licensed under the BSD license. It is one of the most popular frameworks for python, and it is used for research and practical implementation of neural networks and deep learning. It can run on multiple CPU and GPU processors and is designed to handle extensive networks as it can handle matrices as large as 2GB. It is one of the most widely used frameworks for deep learning and is used by popular companies such as Airbnb, Dropbox, Pinterest, and Spotify.
Deeplearning4j is an open-source, distributed deep-learning software platform written in Java and Scala. It is designed to be general-purpose, easy to use, and flexible. It is currently used in many commercial applications, including large-scale image recognition. It uses the popular Java programming language to run on the Java Virtual Machine on all popular computing platforms. This means that users can deploy Deeplearning4j on servers they already have without buying new hardware. DL4J can be used in business environments on both distributed GPUs and CPUs. It allows for rapid prototyping via its intuitive API, and its modular architecture allows for the plug-in of new deep-learning modules. The Skymind company develops Deeplearning4j. The company also provides the Deeplearning4j community with high-quality training materials to help users get started.
Open Neural Network Exchange (ONNX) is an open-source project developed by Microsoft, Facebook, and the community. The project aims to give developers and researchers a platform and tools to convert models between multiple frameworks. To be more precise, it is a standard that allows developers to move their models between frameworks and even between hardware and software. The ONNX format is not a new language or a new framework but rather a standard for representing deep learning models. Once the model is in the ONNX format, it can be read by any framework that supports ONNX.
It consists of a language-independent format that captures the full range of capabilities needed to represent modern neural networks. The ONNX Runtime provides pre-trained models in a cross-framework form to run [almost] anywhere. ONNX is an open community with many contributors from academia and industry, including Adobe, Amazon, Apple, Facebook, Google, IBM, Intel, Microsoft, and more.
Keras is a high-level open-source neural networks API, written in Python and capable of running on either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Going from idea to result with the least possible delay is key to doing good research. Keras has the following features: Its API is very similar to NumPy, making it easy to integrate with existing projects; It provides automatic differentiation (backpropagation) through the intuitive differentiation APIs; It compiles models to efficient, portable, C++ code. It can run on top of multiple back-ends like Tensorflow and Theano simultaneously, which makes it highly flexible, allowing the user to switch between the back-end runtimes depending on the task at hand. It won't matter if you are using deep learning for image recognition, natural language processing, or something else; Keras is the standard interface that you can use across all these. Currently, it is one the most popular deep learning library in the world, with over 53k stars on Github.
PyTorch Lightning 
PyTorch Lightning, a new framework built on top of PyTorch, is gaining popularity among researchers. The framework is designed to enable researchers to write codes in Python, which is the language preferred by the majority of researchers. The framework is designed so that it can be used for quickly training deep learning models, which is the standard in the field of deep learning. Lightning can also structure your PyTorch code so it can abstract the details of training. This makes AI research scalable and fast to iterate on. This framework comes with excellent documentation and community with lots of open-source love!
H2O is an open-source software platform for machine learning, predictive analytics, and artificial intelligence. The framework is designed so that machine learning is approachable to people who may not be experts in the subject. It is supported by H2O.ai, a leading software company in the machine learning space. It supports data science teams in significant industries to build, deploy, and manage predictive models at scale. This framework includes machine learning algorithms, prebuilt ML models, and built-in visualization tools, which allows you to quickly create your own models for your data. It uses a special kind of architecture that leverages distributed computing to process data at scale. The H2O platform is available for free and allows users to deploy predictive modelling to any data, in any location, at any scale.
The Kaldi open source speech recognition software is used by academia and industry worldwide to develop new speech recognition systems. The system is designed to be modular, scalable, and extensible. It is written in C++ and has bindings for Python, MATLAB, Java, and other languages. Kaldi has been used for speech recognition research and development by several large companies, including Microsoft, Google, Apple, and IBM. Kaldi is distributed under the Apache 2.0 license.
It can be used as a drop-in replacement for other libraries such as D3/Three.js to process and display data and build and train machine learning models. Additionally, TensorFlowJS also supports running models in Node.js and even on a Raspberry Pi.
We hope you enjoyed our post about the top deep learning frameworks for 2022. This is a very exciting time for deep learning, with new frameworks being maintained regularly. If you are currently looking into deep learning for your company, be sure to check out the frameworks in this list to see which one best fits your needs. If you have any questions, please feel free to drop us a comment. Thank you for reading!