The Most Popular Deep Learning Software In 2022


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This article provides a list of the most popular deep learning software tools that developers use today. We will show what deep learning libraries are popularly used in deep learning projects today.

What is Deep Learning?

Deep Learning is a nowadays extremely popular machine learning method and is, therefore, an important subfield of AI (Artificial Intelligence). Deep Learning uses machine learning techniques to solve real-world problems by tapping into neural networks that simulate human decision-making, so-called deep convolutional neural networks (CNN). Hence, Deep Learning trains the machine to do what the human brain does naturally.

Deep learning is best characterized by its layered structure that is the foundation of artificial neural networks and forms a deep neural network. Each layer adds to the knowledge of the previous layer.

Deep learning tasks can be expensive, depending on significant computing resources, and the AI models require massive datasets to train themselves on. For Deep Learning, a huge number of parameters need to be understood by a learning algorithm, which can initially produce many false positives. To learn more about what deep learning is, I recommend checking out the article What Is Deep Learning? Definition of Deep Learning (Guide).


Real world deep learning application with video cameras
Deep Learning Model in Computer Vision

The Most Popular Deep Learning Software

  • Tool #1: Neural Designer
  • Tool #2: DeepLearningKit
  • Tool #3:
  • Tool #4: Microsoft Cognitive Toolkit
  • Tool #5: Torch
  • Tool #6: Keras
  • Tool #7: ConvNetJS
  • Tool #8: Deeplearning4j
  • Tool #9: Gensim
  • Tool #10: CAFFE

1. Neural Designer – Explainable AI Platform

Neural Designer is among the most popular deep learning software used extensively for building predictive models. Neural Designer is a code-free app for data science,  machine learning software that allows you to quickly build AI-powered applications. This software enables developers to build AI models based on neural networks to identify patterns, make predictions, and recognize relationships based on the input data.

This software is compatible with the most common data file types and features various technical tools to solve data-related issues.

It owes its popularity to its graphical user interface, allowing users to build predictive models or conduct complex operations quickly, easily, and efficiently. The DL software can be used for solving problems of approximation, classification, forecasting, and association.

Neural Designer is used extensively in several industries, including banking, insurance, healthcare, engineering, etc.

2. DeepLearningKit – GPU Deep Learning Framework for Apple Products

DeepLearningKit is open-source deep learning software that Apple uses for its products, including iOS, OS X, tvOS, and more. This deep learning framework supports pre-trained deep learning models on all apple devices with GPUs.

The DeepLearningKit software is developed in Metal for utilizing GPU more efficiently and developed in Swift for integration with apps. It supports the usage of deep convolutional neural networks for different tasks, including image recognition.

At present, it is trained on the Caffe deep learning framework but will soon have support for other frameworks like Torch and TensorFlow.

3. – End-to-End Platform To Build AI Models and Applications is a free, open-source machine learning software platform that allows developers to build world-class AI models and software applications with remarkable ease. The software platform supports all types of commonly used machine learning tools and statistical algorithms, including deep learning models.

It is built from scratch using Java and can be integrated seamlessly with other popular open-source products like Apache Hadoop and Spark for more flexibility. In addition, the deep learning software platform comes with a user-friendly and convenient interface that makes it easy for anyone to apply deep learning tools to solve complex problems.

Users can build models using either the interactive H2O Flow interface that requires no coding or choose to use their preferred programming languages like R, Python, etc. To read more about low-code or no-code platforms for deep learning, we recommend you checking out Low-Code and No-Code Platforms for AI and Computer Vision.

4. Microsoft Cognitive Toolkit (CNTK) – Commercial-Grade Distributed Deep Learning

The Microsoft Cognitive Toolkit is a deep learning software developed by Microsoft Research. The free commercial-grade toolkit is used to train deep learning models to learn in ways comparable to the human brain.

Previously known as CNTK, it presents neural networks as a series of computational steps via a directed graph. The software toolkit allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Furthermore, it offers high scalability, speed, and accuracy even when working with massive data sets. In addition, it is compatible with multiple programming languages and algorithms.

The CNTK was one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format, an open-source shared model representation for framework interoperability and shared optimization. Co-developed by Microsoft and many others, ONNX allows developers to move deep learning models between frameworks such as CNTK, Caffe2, MXNet, and PyTorch.

5. Torch – Open-Source Machine Learning Software Library

Torch is one of the most efficient open-source deep learning software. This computing framework supports all machine learning algorithms that put GPUs first. In addition, a fast and easy to understand scripting language named LuaJIT and an underlying C/CUDA implementation ensures that it provides a simple and efficient interface to its users.

Built to provide maximum flexibility and speed to the users in building scientific algorithms, the software includes a massive ecosystem of community-driven packages in machine learning, signal processing, computer vision, etc.

Some of the software’s core features include a powerful N-dimensional array, fast and efficient deep learning GPU support, and numerous routines for slicing, indexing, numerical optimization, etc. It is also embeddable with Android and iOS.

6. Keras – Deep Learning API Written in Python

Keras is a Python-based deep learning software tool that is popular for its simplicity and flexibility. This open-source library works as an interface for the machine learning platforms TensorFlow and Theano. The main advantage of deep learning with Keras is its exceptional speed – it allows you to develop and train a neural network model using only a few lines of code.

Hence, Keras often requires about 50% less code to define a model than native APIs of deep learning frameworks require. Here is an example of LeNet-5 trained on MNIST data in Keras – only 70 lines of code.

Keras is one of the best deep learning software for beginners as it makes running new experiments easier and faster. This also makes it popular to use for researchers who constantly train and deploy AI models. The user-friendly and easy-to-use interface makes it one of the most popular choices for students interested in gaining knowledge about deep learning with Python.

Keras provides support for both convolutional and recurrent networks and a combination of both types.

7. ConvNetJS – Javascript Library for Training Deep Learning Models (Neural Networks)

ConvNetJS allows users to train neural network-based deep learning models using Javascript, right from their browser windows. It is a hassle-free option that doesn’t need any installations, compilers, deep-learning GPUs, or specific software requirements.

Originally created by a Ph.D. student of Stanford, Andrej Karpathy, who is now Director of AI at Tesla. The ConvNetJS software the library gradually expanded through community contributions. It supports standard neural network modules and helps specify and train convolutional networks for processing images.

8. Deeplearning4j – Java Framework for Deep Learning

Deeplearning4j is the first commercial-grade open-source deep learning software library created in Java. It is compatible with all JVM languages, including Scala, Clojure, and Kotlin. This software is designed for bringing AI to business environments for use on distributed GPUs and CPUs.

The software makes use of popular computing frameworks such as Apache Spark and Hadoop to accelerate training. It can be used to create multi-layer deep neural networks with ease. In addition, it offers users the flexibility to combine variational autoencoders, sequence-to-sequence autoencoders, convolutional networks, and recurrent networks as needed.

9. Gensim – Python-Based Unsupervised Topic Modeling and Nlp

Gensim is a free-to-use open-source library written in Python. It is useful for unsupervised topic modeling and natural language processing using machine learning tools. This software is different from other machine or deep learning software as it doesn’t target in-memory processing. Instead, it is designed for extracting semantic topics from documents.

One of the core features of Gensim is its ability to handle large collections of textual data. The deep learning python software is more convenient for text processing than other available packages like Scikit-learn, etc. It finds application in different fields such as medicine, insurance, etc.

10. Caffe – Fast Open Framework for Deep Learning

The Caffe (Convolutional Architecture for Fast Feature Embedding) deep learning software framework is built with keeping expression, speed, and modularity in mind. It is an open-source framework, initially developed by Berkeley AI Research (BAIR) under the University of California, Berkeley. The software is currently released under the BSD 2-Clause license.

The software is written in the C++ programming language and supports numerous deep learning architectures, especially related to image segmentation and classification. It can process close to 60M images in one day with a single NVIDIA AI GPU K40. The high speed and image processing capability make it a suitable choice for industrial implementation and research purposes. In addition, it can be used to train different types of neural networks.

Using multiple deep learning GPUs is currently not officially supported in Keras using existing Keras backends (Theano or TensorFlow), even though most deep learning frameworks have multi-GPU support, including TensorFlow, MXNet, CNTK, Theano, PyTorch, and Caffe2.

What’s next?

There are multiple deep learning software and machine learning tools available that are popularly used in AI projects, to deploy and apply neural networks for deep learning tasks.

To read more about related topics, you might want to read another article:

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