Top Ten Open Source Deep Learning Tools to be aware of in 2022 | Technology

Top Ten Open Source Deep Learning Tools to be aware of in 2022 | Technology
Top Ten Open Source Deep Learning Tools to be aware of in 2022

In 2022, here are the top ten open source deep learning tools to be aware of

Deep learning requires proper tools.  Therefore, adapting to open source tools is more viable than buying 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.  Precisely for this reason, many companies choose to use open source tools.  Since deep learning requires the right tools, adapting to open source tools is more viable than buying proprietary tools, which sometimes slow down the development cycle while increasing the total cost of ownership (TCO).  With open source deep learning tools, existing code can be redistributed and retuned so programmers can focus on challenges that are unique.  Here is the list of top 10 open source deep learning tools in 2022, which every deep learning developer needs in his kitty.

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1. TensorFlow 

Largely open source, this project was originally developed by Google and hosts a galaxy of tools, libraries, and community resources to help developers easily build and deploy ML-based applications.  It is quite easy to create 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 that runs on top of Theano or Tensorflow.  Although Keras cannot handle low-level computation, it acts as a high-level API wrapper for low-level wrappers and can scale to large pools of GPUs paving the way for flexible and robust investigation processing.

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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 Center (BVLC), it is the most widely used open source deep learning tool in areas such as commissioning prototypes, particularly in areas such as vision, speech, and multimedia.

4. Pytorch

It is an open source deep learning library used to develop and train neural networks for AI projects.  With Pytorch, it is possible to build complex architectures as it uses dynamic computation, unlike other deep learning frameworks that use static computation methods.  Developed by Facebook's AI research labs, it's backed by big companies like Microsoft, Salesforce, and Uber.

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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 really speeds up the investigation process for faster model training using flexible programming models and multiple languages.

6. ai

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

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7. Deeplearning 4j

Eclipse Deeplearning is an open source toolkit for running deep learning models on the Java Virtual Machine and is the only tool that enables Java interoperability with Python via CPython bindings and model import support .  With these tools, one can import and retrain models in PyTorch, Tensorflow, and Keras and then deploy them to JVM, mobile, IoT, and Apache Spark microservice environments.  It is industry focused and commercially supported, it can solve problems involving massive amounts of data.

8. Theano

This open source Python library is designed for fast numerical computation and can perform efficient symbolic differentiation using GPU.  It greatly simplifies the process by allowing users to apply these libraries directly to create deep learning models or wrapper libraries.  It squeezes as much efficiency as possible out of the hardware by using a host of smart code optimization techniques.

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9. scala

Create statically typed dynamic neural networks from the map and other higher-order functions.  Monad-like neural networks, built with higher-order functions and parallel calculations with application-like classes, are feasible with Deeplearning.scala.

10. BigDL 

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

Source: Analytics Insight, Direct News 99