Top 10 Deep Learning Frameworks to Learn in 2022
Strong understanding on deep learning frameworks are a must-have skill for AI professionals.
Deep learning frameworks with an interface or a tool helps data scientists and ML developers to bring the deep learning models into life. Deep learning is a sub-branch of machine learning that puts efficiency and accuracy on the table, when it is trained with vast amounts of big data. Here are the top 10 deep learning frameworks you can learn about in 2022.
TensorFlow developed by the Google Brain team, is inarguably one of the most popular deep learning frameworks. It supports Python, C++, and R to create deep learning models along with wrapper libraries. It is available on both desktop and mobile. The most popular use case of TensorFlow is the Google Translate integrated with capabilities like NLP, text classification, summarization, speech/image/handwriting recognition and forecasting. Its visualization toolkit, TensorBoard, provides effective data visualization of network modeling and performance.
TensorFlow Serving, another TensorFlow tool, is deployed for the rapid development of new algorithms/experiments while retaining the same server architecture and APIs.
Deeplearning4j is a deep learning library for the Java Virtual Machine (JVM) developed in Java and supports other JVM languages like Scala, Clojure, and Kotlin.
Parallel training through iterative reductions, micro-service architecture adaptation coupled with distributed CPUs and GPUs are some of the salient features when it comes to Eclipse Deeplearning4j deep learning framework. Deeplearning4j is widely adopted as a commercial, industry-focused, and distributed deep learning platform which comes with its own deep network support through RBM, DBN, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Recursive Neural Tensor Network (RNTN) and Long Short-Term Memory (LTSM). It can be administered on top of both Hadoop and Spark.
Microsoft Cognitive Toolkit (CNTK)
The Microsoft Cognitive Toolkit (earlier known as CNTK) is an open-source deep learning framework to train deep learning models. CNTK is used for Convolution Neural Networks and training for image, speech, and text-based data.
The Microsoft Cognitive Toolkit is known to provide higher performance and scalability while operating on multiple machines. The implementation of Reinforcement Learning models or Generative Adversarial Networks (GANs) can be done quickly using the CNTK.
The Microsoft Cognitive Toolkit is highly efficient and scalable for multiple machines, supported by interfaces such as Python, C++, and Command Line and fit for image, handwriting and speech recognition use cases.
Written in Python, Keras neural networks library supports both convolutional and recurrent networks that are capable of running on either TensorFlow or Theano. Keras deep learning framework is built to provide a simplistic interface for quick prototyping by constructing active neural networks which can work with TensorFlow. Keras is lightweight, easy-to-use, and with its minimalist approach, it is a part of TensorFlow’s core API.
Use cases of Keras range from classification, text generation, and summarization, tagging, translation along with speech recognition, and others. Keras is easy-to-understand and consistent APIs that seamlessly integrates with TensorFlow workflow and comes with built-in support for multi-GPU parallelism and distributed training.
Shogun is an open-source machine learning framework compatible with the C++ programming language. Its free platform helps developers design algorithms and data structures, primarily for ML problems in education and research domains.
Shogun was designed by Gunnar Raetsch and Soeren Sonnenburg in 1999 to support vector machines for classifications and regression problems, plus large-scale learning. It allows developers to connect with other machine learning libraries, including LibLinear, LibSVM, SVMLight, LibOCAS, and more.
Furthermore, Shogun also supports other languages and frameworks, including R, Python, Java, Octave, C#, Ruby, MatLab, Lua, and more.
ONNX or the Open Neural Network Exchange was developed by Microsoft and Facebook as an open-source deep learning ecosystem. This deep learning framework comes with definitions on in-built operators, standard data types as well as definitions of an expandable computation graph model. ONNX models are natively supported in the Microsoft Cognitive Toolkit, Caffe2, MXNet, and PyTorch. It also provides converters for different machine learning frameworks like TensorFlow, CoreML, Keras, and Sci-kit Learn.
This DL framework has gained popularity owing to its flexibility and interoperability. Using ONNX, one can easily convert their pre-trained model into a file, which can then be merged with their app. ONNX is a powerful tool that aids to prevent the framework lock-in by enabling model sharing and providing easier access to hardware optimization.
MXNet (pronounced as mix-net) is supported by Python, R, C++, and Julia. What makes MXNet one of the most preferred deep learning frameworks is its functionality of distributed training. It enables the user to code in a variety of programming languages that includes Python, C++, R, Julia, and Scala, which means users can train their deep learning models with whichever language they are comfortable in without having to learn something new from scratch.
With the backend written in C++ and CUDA, MXNet is able to scale and work with a myriad of GPUs, making it immensely popular among enterprises. It supports Long Short-Term Memory (LTSM) networks, along with both RNN and CNN. This deep learning framework is known for its capabilities in imaging, handwriting/speech recognition, forecasting as well as NLP.
CAFFE is well known for its laser-like speed, and comes supported with interfaces like C, C++, Python, MATLAB, and Command Line. Its speed and applicability in modeling Convolution Neural Networks (CNN) have made it immensely popular in recent years.
The most significant benefit of using Caffe’s C++ library is accessing the deep net repository ‘Caffe Model Zoo.’ Caffe Model Zoo contains networks which are pre-trained. CAFFE’s speed is its ultimate USP, it can process over sixty million images on a daily basis with a single Nvidia K40 GPU. However, it does not support fine granularity network layers like those found in TensorFlow or CNTK.
Chainer is an open-source Deep Learning framework which is written in Python on top of NumPy and CuPy libraries. It is the first Deep Learning framework which has introduced the define-by-run approach where users first need to define the fixed connections between mathematical operations like matrix multiplication and nonlinear activations in the network to subsequently run the actual training computation. Chainer has four extension libraries – ChainerMN, ChainerRL, ChainerCV, and ChainerUI.
With its ChainerMN library the deep learning framework can be used on multiple GPUs and delivers a super-fast performance, as compared to other Deep Learning frameworks like MXNet and CNTK.
H2O is another open-source machine learning framework. It is an AI tool designed to assist businesses in their decision-making processes based on available data. H2O platform is primarily used for risk and fraud analysis, predictive modeling, healthcare, insurance analytics, advertising, and customer intelligence.