In today’s digital era, artificial intelligence is advancing at a greater pace by having deep learning as its primary contributor. Since deep learning is one of the subfields of artificial intelligence, most of the AI tasks and applications involve deep learning models. Deep learning works similar to the human brain, which perceives and transmits information through countless neuron interactions. The applications of deep learning include image processing, text classification, object segmentation, natural language processing, and much more. To build such high-end applications and use cases, you have to employ appropriate deep learning libraries at different phases of an end-to-end deep learning model development lifecycle. There are a vast collection of libraries available for implementing deep learning tasks, from which you can select the most suitable and efficient library based on your use cases and business models.
This article mainly focuses on the top 8 deep learning libraries that are primarily used by developers at different phases of the deep learning lifecycle.
Keras is one of the most prominent open-source libraries used mainly for implementing deep learning-related tasks. It initially started its journey as a Google project named ONEIROS (Open-Ended Neuro Electronic Intelligent Robot Operating System) for enabling faster experimentation with neural networks. In 2017, Keras was added to Google’s TensorFlow machine framework, making it a high-level API for building and training deep learning models.
Since Keras runs on top of the TensorFlow framework, Keras APIs can be used to effectively run both machine learning and deep learning-related tasks. Keras is highly scalable to run on both high-level GPUs and CPUs for developing complex neural network models with less computation time. Because of such enhanced features and functionalities, Keras empowers researchers and engineers to fully exploit scalability and cross-platform capabilities, thereby enabling them to achieve high accuracy and performance while building deep learning models. In addition, Keras is being used in most popular companies like YouTube, NASA, and Waymo because of its industry-strength performance and scalability.
Keras is highly compatible with Python 3.6 to 3.9 versions, Windows, Ubuntu, and Mac Operating systems. Since Keras is an open-source project, it offers greater community support by means of forums, Google groups, and Slack channels. Keras also provides users with straightforward and well-structured documentation, allowing beginners to easily learn and implement deep learning tasks.
Developed by the Google Brain team, TensorFlow is an open-source Python library for implementing high-level numerical computations and large-scale deep learning tasks. Ever since its development, TensorFlow was only used for inter-organizational purposes in Google. However, it was made open-source under the Apache License 2.0 in 2015. Although the TensorFlow framework is primarily used to build and develop deep learning, it also has flexible tools and libraries for building end-to-end machine learning models.
With TensorFlow, you not only build and develop machine learning and deep learning models but also can perform probabilistic reasoning, predictive modeling, and statistical analytics. Since TensorFlow consists of high-level APIs like Keras and Theano, it can be effectively used at any phase of the model development life cycle. In addition, since TensorFlow supports cross-platform deployment, you can easily build and deploy deep learning models in any production platform, such as cloud and on-premises systems,
TensorFlow is made to be compatible with macOS, Windows, 64 bit-Linux, and mobile computing platforms, including Android. To make developers and researchers effectively work with TensorFlow, its official documentation clearly explains the features, functionalities, and implementation methodologies of the respective library.
PyTorch is one of the most popular and open-source deep learning libraries developed by the AI research team of Facebook in 2016. The name of the respective library is based on the popular deep learning framework called Torch, a scientific computation and scripting tool written in the Lua programming language. However, Lua is a complex language to learn and get hands-on and does not offer enough modularity to interface with other libraries. To eradicate such complications, researchers of FaceBook developed and implemented the Torch framework using Python, thereby naming it as PyTorch.
PyTorch not only allows you to implement deep learning-related tasks but also enables you to build computer vision and NLP (Natural Language Processing) applications. In addition, the primary features of PyTorch include tensor computation, automatic differentiation, and GPU acceleration, which makes it stand apart from other top deep learning libraries.
You can flexibly run PyTorch on Linux, Windows, macOS, and any of your preferred cloud computing platforms. PyTorch also offers you standard documentation that specifies its features, functionalities, and algorithms, allowing any user to learn and try implementing deep learning models on their own.
Developed by Apache Software Foundation, MXNet is one of the open-source deep learning libraries in Python that allows you to define, train, build, and deploy deep neural networks. With MXNet, you can develop and deploy deep learning models in any platform like cloud infrastructure, on-premises, and mobile devices. Since MXNet has ultra-scalability and distributive features, it can be seamlessly scaled across multiple GPUs and machines, leveraging them to achieve fast-model training and high performance.
MXNet provides you with a greater community that enables you to participate in discussion forums, collaborate with other researchers, and learn the features and functionalities of the respective library via tutorials and documentation.
5. Microsoft CNTK
Released by Microsoft in 2016, CNTK (Cognitive Toolkit), previously known as Computational Network ToolKit, is an open-source deep learning library used to implement distributed deep learning and machine learning tasks. With the CNTK framework, you can easily combine the most popular predictive models like CNN (Convolutional Neural Network), feed-forward DNN (Deep Neural Network), and RNN (Recurrent Neural Network) to effectively implement end-to-end deep learning tasks.
Although CNTK is primarily used to build deep learning models, it can also be used for implementing machine learning tasks and cognitive computing. Though CNTK’s framework functions are written in C++, it also supports a wide range of programming languages like Python, C#, and Java. Furthermore, you can use CNTK for developing efficient deep learning models by either importing it as a library into your preferred development frameworks or using it as a standalone deep learning tool, or launching it in cloud platforms. Due to its platform compatibility and performance, CNTK is being used by the most prominent companies like Cyient and Raytheon.
CNTK provides you with standard documentation and is also available as an open-source repository in GitHub, making it easier for developers and researchers to learn and implement high-level deep learning methodologies.
Developed by Jeremy Howard and Rachel Thomas in 2016, Fastai is an open-source library primarily used for building deep learning and artificial intelligence models. Since Fastai is built on top of PyTorch, users can leverage the advanced features of both frameworks, thereby achieving high accuracy models with remarkable speed and performance. Apart from its other prominent features, Fastai is the first deep learning library to offer a standalone interface for building various end-to-end deep learning applications, including computer vision, text classification, neural network, and time series models.
As its name implies, Fastai helps developers build efficient and high-level models using minimal amounts of code with faster experimentation capability. Fastai achieves high-speed experimentation since it can automatically figure out suitable pre-processing techniques and training parameters for the specific dataset, making it more accurate than other deep learning libraries.
Fastai offers basic to advanced practical courses for beginners and developers. It also provides users with clear documentation, incorporating features and algorithms of the Fastai library along with their use cases.
Developed in 2010, Theano is an open-source deep learning library for implementing and evaluating more complex mathematical and scientific computations that involve multi-dimensional arrays. With Theano, you can achieve transparent GPU usage by manually setting the GPU usage limit and frequency. You can develop a highly scalable and reliable training framework by utilizing multiple GPUs across the cluster, which results in accelerating the training speed of deep learning models.
With Theano, you can express and define your model in terms of mathematical expressions and computational graphs, making it easy to evaluate and assess the training capability of the respective model. Since Theano offers developers a general-purpose computing framework for implementing complex neural network models with remarkable speed and accuracy, it is extensively utilized in the Python community, especially for deep learning research.
Theano provides you with the comprehensive documentation that incorporates all its functions, methodologies, and algorithms, making it easy for beginners to understand and implement deep learning techniques.
Developed by BAIR (Berkeley AI Research), Caffe is one of the most popular deep learning libraries for Python, which is used to implement machine vision and forecasting applications. Cafe\fe serves as a one-stop framework for training, building, evaluating, and deploying deep learning models. With Caffe, you can build and evaluate your deep neural networks with a sophisticated set of layer configurations options. However, you can also access pre-made neural networks from the Caffe community website based on your use cases and model preferences.
Since Caffe can be scaled across multiple GPUs and CPUs, you can achieve greater training and processing speed, allowing you to train deep learning models in less time. Because of its features, enhanced training speed, and performance, Caffe is being used by popular organizations like Adobe, Yahoo, and Intel.
Caffe offers you a well-documented user guide, incorporating its philosophy, architecture, methodologies, and use cases. It’s also accessible as an open-source repository on GitHub, letting users experiment with Caffe’s functions and algorithms.