Top 10 Open-Source Deep Learning Tools to Know in 2022

Deep learning requires appropriate tooling. So, adapting to open-source tools is more viable than buying the proprietary ones

Artificial intelligence is currently in a rediscovery stage, where researchers, as well as programmers, need a lot of room to experiment and explore. This is precisely why many companies choose to resort to open-source tools. Since deep learning requires appropriate tooling, adapting to open-source tools is more viable than buying the proprietary tools, which at times slow down the development cycle, all while increasing the total cost of ownership (TCO). With open-source deep learning tools, it is possible to redistribute and readjust the existing code so that programmers can focus on the challenges that are unique. Here is the list of top10 open-source deep learning tools in 2022, that every deep learning developer needs in his kitty.

1. TensorFlow: Open-sourced in large parts, this project is originally developed by Google, and hosts a galaxy of tools, libraries, and community resources to help developers easily build and deploy ML-powered applications. It is quite easy to build ML models using high-level APIs like Keras with immediate model iteration and easy debugging.

2. Keras: Keras is an open-source neural network library developed in Python which runs on the top of Theano or Tensorflow. Keras though cannot handle low-level computation acts as a high-level API wrapper for the low-level wrappers and can scale to large clusters of GPUs paving way for the flexible and robust research process.

3. Cafee: A deep learning framework that comes with an expressive architecture, extensible code, and enviable processing speed. Developed by Berkeley AI Research (BAIR)/ The Berkeley Vision and Learning Centre (BVLC), it is the most widely used open-source deep-learning tool in areas such as start-up prototypes, particularly in areas like vision, speech, and multimedia.

4. Pytorch: It is an open-source deep-learning library used for developing and training neural networks for AI projects. With Pytorch, it is possible to build complex architectures as it uses dynamic computation, unlike other deep learning frameworks which use static computation methods. Developed by Facebook AI Research labs it is backed by biggies like Microsoft, Salesforce, and Uber.

5. Apache MxNet: It is an open-source deep-learning tool suitable for flexible research prototyping and production. Enhanced with a rich ecosystem of tools and libraries, it truly accelerates the research process for faster model training using flexible programming models and multiple languages.

6. ai: Fast.ai is a layered API framework that facilitates practitioners to work with both high level as well as low-level components. As a result, the developers can mix and match components to develop new approaches. Written in Python, it is one of the most flexible deep learning frameworks that make access to AI research easy for all, especially people from different backgrounds.

7. Deeplearning 4j: Eclipse Deeplearning is an open-source suite of tools for running deep learning models on Java Virtual Machine and it is the only tool that allows interoperability of Java with Python through CPython bindings and model import support. With these tools, one can import and retrain models in PyTorch, Tensorflow, and Keras and then deploy them in JVM Microservice environments, mobile devices, IoT, and Apache Spark. It is industry focussed and commercially supported for, it can solve problems involving massive amounts of data.

8. Theano: This open-source Python library is meant for fast numerical computation and can perform efficient symbolic differentiation using GPU. It greatly simplifies the process by letting users apply these libraries directly to create Deep Learning models or wrapper libraries. It squeezes as much efficiency as possible from hardware by using a host of smart code optimization techniques.

9. scala: Create statically typed dynamic neural networks from the map and other higher-order functions. Monad-like neural networks, created using higher-order functions and parallel calculations using applicative type classes are feasible with Deeplearning.scala.

10. BigDL: A distributed deep learning library for Apache spark, it provides tools to run deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

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