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Top 10 Easy Deep Learning Frameworks for Beginners in 2021


November 7, 2021

Deep Learning

The growth of machine learning and deep learning has enabled organizations to provide smart solutions and predictive personalization’s to their customers. Deep learning frameworks are interfaces, libraries, or tools, which are generally open-source that people with little to no knowledge of machine learning and AI can easily integrate. Deep learning frameworks can help you upload data and train a deep learning model that would lead to accurate and intuitive predictive analysis. This article lists the top deep learning frameworks for beginners in 2021.



Google’s Brain team developed a deep learning framework called TensorFlow, which supports languages like Python and R, and uses dataflow graphs to process data. This is very important because as you build these neural networks, you can look at how the data flows through the neural network. TensorFlow’s machine learning models are easy to build, can be used for robust machine learning production, and allow powerful experimentation for research.



Keras is an open-source neural network library written in Python which can run on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, R, and PlaidML. François Chollet, a Google engineer, designed Keras to enable fast experimentation with neural networks. It is very user-friendly, modular, and extensible. Keras also takes pride in being simple, flexible, and powerful. Due to these features, Keras is viewed as the go-to deep learning library by newcomers.



Built atop Tensorflow and developed by DeepMind, the Sonnet framework is a high-level solution for building complex structures for neural networks. The Sonnet solution develops and enhances Python objects concerning certain parts of a neural network, connecting each object to a computational Tensorflow graph. This helps to simplify the development of complex networks. Sonnet offers a simple, powerful programming model built around a single concept. Modules are self-contained and decoupled, and you can even write modules that pass to other models during a construction process.



PyTorch is an open-source neural network library primarily developed and maintained by Facebook’s AI Research Lab (FAIR) and initially released in October 2016. FAIR built PyTorch on top of the Torch library, another open-source machine learning library, a scientific computing framework, and a scripting language based on the Lua programming language, initially designed by Ronan Collobert, Samy Bengio, and Johnny Mariéthoz.

Since PyTorch is developed by Facebook and offers an easy-to-use interface, its popularity has gained momentum in recent years, particularly in academia.


Deeplearning4j (DL4J)

A machine learning group that includes the authors Adam Gibson Alex D. Black, Vyacheslav Kokorin, Josh Patterson developed this Deep Learning Framework Deeplearning4j. Written in Java, Scala, C++, C, CUDA, DL4J supports different neural networks, like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM (Long Short-Term Memory).



Caffe is another modern deep learning framework focusing on speed, modularity, and expression. Developed by the Berkeley AI Research community, Caffe is most popular among people who have explored machine and deep learning in the past. The framework is best known for its speed and efficiency. It can process more than 60 million images a day and deliver excellent computer vision opportunities.



A very recent addition to the list of deep learning frameworks, Gluon is an open-source Deep Learning interface that helps developers to build machine learning models easily and quickly. It offers a straightforward and concise API for defining ML/DL models by using an assortment of pre-built and optimized neural network components.

Gluon allows users to define neural networks using simple, clear, and concise code. It comes with a complete range of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers. These help to eliminate many of the underlying complicated implementation details.



The creation of both Microsoft and Facebook, the ONNX or Open Neural Network Exchange, is an open solution created for the development and presentation of various deep and machine learning models. There’s a scalable computation graph model, built-in operators and standard data types, and simple transitions between different AI methods. You can train models in one environment, then move them to another for inference purposes.



Another open-source framework intended to train deep neural networks, MXNet is a highly scalable and rapid solution for model training. Aside from offering a flexible programming model, MXNet also supports various programming languages, including Python, C++, JavaScript, Matlab, Wolfram, and many others.

A hybrid front-end and distributed training solutions make MXNet a powerful choice for a wide range of developers. This environment is lean and flexible, with support for all kinds of state-of-the-art DL models, including convolutional neural networks. You can also expect fast context switching, imperative and symbolic programming, and endless tools and libraries.


Microsoft Cognitive Toolkit

Otherwise known as CNTK, the Microsoft Cognitive toolkit is a commercial-grade open-source solution for deep learning on a distributed basis. The Toolkit defines networks as a selection of directed graph computational steps, allowing users to easily combine and discover model types. CNTK also implements automatic differentiation, parallelization, and more across various servers and GPUs.

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