10 Best Deep Learning Software

eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

The need for top-rated deep learning software is increasing as the focus on advanced artificial intelligence and machine learning solutions continues to grow. Deep learning is one of the most promising fields in artificial intelligence, revolutionizing industries in various industries, including healthcare, finance, robotics, and self-driving cars.

By 2025, deep learning technology is predicted to have a global market revenue of $10.2 billion. This figure suggests that deep learning will see even more widespread adoption in the future. To help you stay ahead of your competition and develop AI models to enhance your business, we analyzed the best deep learning software currently leading the market.

Jump to:

• Best Deep learning software: Comparison chart
• PyTorch: Best for deep learning research and prototyping
• TensorFlow: Best for high-level model development
• H2O.ai: Best for building AI models and applications
• Keras: Best for beginners and researchers
• Neural Designer: Best for building predictive models
• Gensim: Best for natural language processing (NLP) tasks
• Caffe: Best deep learning framework for speed
• Apache SINGA: Best for heterogeneous computing environments
• Deeplearning4j: Best for Java-based projects
• Apache MXNet: Best for large scale deep learning and flexibility
• How to choose the best deep learning software for your business
• Review methodology
• Bottom Line: Best deep learning software

Best Deep learning software: Comparison chart

PyTorch: Best for deep learning research and prototyping

PyTorch is an open source machine learning (ML) framework based on Python and Torch library and is used for building deep learning models such as computer vision and natural language processing. It was originally developed by Meta AI, but it’s currently part of the Linux Foundation. The PyTorch ecosystem includes many high-level APIs and tools that simplify tasks like data loading, natural language processing, and reinforcement learning.

PyTorch pricing

PyTorch is free to install and use.

PyTorch key features

• Interoperates with major cloud platforms such as AWS, GCP, Alibaba Cloud, and Azure.
• Supports various neural network architectures like convolutional networks, recurrent networks, and transformers.
• Dynamic computational graph.

PyTorch pros

• PyTorch has a vibrant community of researchers, developers, and enthusiasts who actively contribute to its development.
• Easy debugging and rapid prototyping.
• Large community and ecosystem.

PyTorch cons

• Less mobile and embedded device support.
• Limited visualization tools.

For more information, also see: Best Machine Learning Platforms

TensorFlow: Best for high-level model development

TensorFlow is an end-to-end open-source machine learning framework developed by the Google Brain team. TensorFlow is used to facilitate the development of deep learning models. It provides an ecosystem of tools, libraries, and resources, enabling researchers and developers to build and deploy machine learning applications efficiently.

TensorFlow pricing

TensorFlow is free, open-source software.

TensorFlow key features

• TensorFlow includes automatic differentiation capabilities.
• It supports many different platforms and programming languages, including Python, C++, and JavaScript.
• Contains pre-trained models and datasets.
• Availability of statistical distributions.

TensorFlow pros

• Keras friendly.
• It offers visualization tools.
• Flexible deployment – web, mobile, edge and servers.

TensorFlow cons

• Limited support for Windows OS users.
• Only offers support for NVIDIA GPU and Python GPU.

On a related topic: Top Natural Language Processing Companies

H2O.ai: Best for building AI models and applications

H2O.ai is a fully open source, distributed in-memory machine learning platform that supports widely used statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, and deep learning. Its Driverless AI tool, which is an automated machine learning platform, uses advanced algorithms and techniques to automate various steps in the machine learning workflow, including feature engineering, model selection, hyperparameter tuning, and model interpretation.

H2O.ai pricing

The H2O.ai platform has an open-source, freely available version. This version includes the core functionality of H2O and allows users to build models using a wide range of algorithms. H2O.ai also offers enterprise-level solutions and services, which may have additional pricing considerations. For instance, the H2O.ai AI Cloud costs $50,000 per unit, you must buy a minimum of four units.

H2O.ai key features

• Real-time data scoring.
• Automated machine learning.
• L1 or L2 regularization.
• Distributed in-memory computing.

H2O.ai pros

• Its user-friendly interface makes it accessible to non-experts.
• High-performance capabilities.

H2O.ai cons

• H2O.ai proprietary tools are expensive.
• Advance features require a strong statistics and machine learning background.

Also see: Best Artificial Intelligence Software 2023

Keras: Best for beginners and researchers

A top deep learning software worth considering is Keras. It stands out for its simplicity and ease of use. Keras, a Python-based deep learning library, was developed to enable fast experimentation and ease of use for building and training deep neural networks. It works as an interface for the machine learning platforms TensorFlow and Theano. These types of tools are expanding as artificial intelligence and generative AI sees dramatic growth.

Keras pricing

Keras is available for free.

Keras key features

• Built on top of the TensorFlow platform.
• Users can export Keras models to JavaScript to run directly in the browser to TF Lite to run on iOS, Android, and embedded devices.
• Support convolutional and recurrent networks.
• Allows users to define, compile, and train neural networks with a concise and intuitive syntax.

Keras pros

• User-friendly API.
• Designed to be backend-agnostic, meaning it can run on top of various deep learning frameworks.
• High-level abstractions.

Keras cons

• Limited flexibility.
• Limited documentation.

Also see: Top Generative AI Apps and Tools

Neural Designer: Best for building predictive models

Neural Designer is an AI platform that allows you to build AI-powered applications without without creating block diagrams or coding. This deep learning software can be used to discover relationships, recognize patterns, and predict trends from your data. Neural Designer is used extensively in several industries, including environment, banking, energy, insurance, healthcare, manufacturing, retail and engineering.

Neural Designer pricing

Neural Designer offers various pricing plans for different categories of users.

Standard licenses

• Small: $2,495 per user per year or $6,245 per user, lifetime access.
• Medium: $4,995 per user per year or $12,495 per user, lifetime access.
• Large: $7,495 per user per year or $18,745 per user, lifetime access.

Academic licenses

• Research: $2,495 per user per year or $6,245 per user, lifetime access.
• Teaching: $4,995 per module per year or $12,495 per user, lifetime access.
• Campus: Custom quotes.

Training programs

• Standard Training Program: Costs $1,495 for 8 hours.
• Personalised Training Program: Custom pricing.

Neural Designer key features

• GPU acceleration (NVIDIA CUDA).
• Multi-core parallelization (Intel MKL).
• High performance computing.

Neural Designer pros

• User-friendly graphical interface.
• It can handle different types of machine learning tasks, including classification, approximation, text classification, and forecasting.

Neural Designer cons

• Neural Designers require a license or subscription fee, which could be a consideration for individual users or small businesses on a budget.
• Dependency on user expertise.

Also see: Generative AI Startups

Gensim: Best for natural language processing (NLP) tasks

Gensim is an open-source Python library designed for topic modeling and natural language processing (NLP) tasks. For context, topic modeling is a technique used to discover hidden thematic structures in large collections of text documents. Gensim allows you to analyze, compare, and interpret large collections of textual data by enabling the creation of high-quality semantic representations.

Gensim provides implementations of popular topic modeling algorithms, such as Word2Vec, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and others, for topic modeling and natural language processing tasks.

Gensim pricing

Gensim is a free deep learning software licensed under the OSI-approved GNU LGPLv2.1 license, making it free for both personal and commercial use.

Gensim key features

• Gensim runs on Linux, Windows and OS X.
• Also runs on platforms that support Python 3.6+ and NumPy.
• Provides implementations of popular algorithms like Word2Vec, LSA, and LDA.
• Non-negative matrix factorization.

Gensim pros

• Gensim is designed to handle large datasets efficiently, using memory-friendly techniques like streaming, which reduces memory consumption during processing.
• Memory independence.

Gensim cons

• While Gensim excels in traditional NLP techniques like topic modeling and word embeddings, it may not be the best choice for a wide array of Machine learning tasks.
• Lack of extensive documentation.

For more information: AI vs. ML

Caffe: Best deep learning framework for speed

Caffe is an open-source deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. Yangqing Jia created the project during his Ph.D. at UC Berkeley.  It is written in C++ and provides a Python interface for ease of use. Caffe is designed to be efficient and flexible, allowing users to define, train, and deploy deep learning models for tasks such as image classification, object detection, and segmentation. It has gained popularity for its speed and ease of use in training and deploying convolutional neural networks (CNNs).

This deep learning software is mainly used for academic research projects, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia.

Caffe pricing

Caffe is released under the BSD 2-Clause license, making it free to use.

Caffe key features

• Expressive architecture.
• Extensible code.
• Deployable on mobile devices.

Caffe pros

• Can process over 60M images per day with a single NVIDIA K40 GPU.
• Wide community support.

Caffe cons

• Limited language support.
• Steep learning curve.

Also see: Generative AI Companies: Top 12 Leaders

Apache SINGA: Best for heterogeneous computing environments

Apache SINGA is an open-source deep learning library and distributed training framework. The project ​ was designed to support and accelerate the training of large-scale machine learning models, particularly deep learning neural networks, across distributed computing resources. The deep learning software stack includes three major components: core, IO and model. It is widely used by various companies, including National University Hospital, Net Ease, Foodlg and more.

Apache SINGA pricing

It is free to use.

Apache SINGA key features

• Automatic gradient calculation.
• Memory optimization.
• Can be easily installed using Conda, Pip, Docker and from Source.
• Supports loading ONNX format models and saving models defined using SINGA APIs into ONNX format.
• Neural network model zoo.

Apache SINGA pros

• SINGA supports various popular optimizers, including stochastic gradient descent with momentum, Adam, RMSProp, and AdaGrad.
• Time profiling capability.
• Multiple programming language support.

Apache SINGA cons

• Small community compared to some other deep learning frameworks.
• Limited documentation.

Also see: 100+ Top AI Companies Today

Deeplearning4j: Best for Java-based projects

Released under Apache License 2.0, Deeplearning4j (DL4J) is an open-source, distributed deep learning library written for Java and Java Virtual Machine (JVM) languages. DL4J includes implementations of various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. The library supports distributed computing using Apache Hadoop and Apache Spark. This allows users to leverage multiple machines and GPUs to speed up the training process and handle large-scale data sets.

Deeplearning4j pricing

It is free to use.

Deeplearning4j key features

• GPU support for scaling on AWS.
• DL4J supports interoperability with other popular deep learning frameworks.
• Deeplearning4j can be used through multiple API languages, including Java, Scala, Python, Clojure and Kotlin.

Deeplearning4j pros

• Excellent community support and documentation.
• Incorporates ETL as part of the library.

Deeplearning4j cons

• Limited capabilities compared to TensorFlow or PyTorch.
• Less Python-Centric.

MXNet: Best for large scale deep learning and flexibility

MXNet is an open-source deep learning framework used for training and deploying artificial neural networks. It is designed to scale from large clusters of GPUs to multiple machines, and it supports various programming languages such as Python, R, Scala, and Julia. MXNet provides automatic differentiation, a crucial feature for training deep learning models, enabling the computation of gradients based on the model’s parameters.

MXNet pricing

It is free to use.

MXNet key features

• Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl.
• Hybrid front-end.
• Auto-Differentiation capability.
• Distributed training capability.

MXNet pros

• It supports multiple platforms, including Linux, MacOS. Windows and Cloud.
• Strong performance.

MXNet cons

• Steep learning curve.
• Limited documentation.

For more information, also see: Top AI Software

How to Choose the Best Deep Learning Software for Your Business

When shopping for the best deep learning software for your business, keep in mind that the best tool for you depends on your unique business needs. There are best practices to follow when looking for the best deep learning software that, if followed rigorously, will lead you to the best deep learning software for your organization.

• Start by defining your requirements.
• If your team is not already well-versed in deep learning, consider ease of use.
• Evaluate the scalability and performance of the software.
• Check which deep learning frameworks and libraries the software supports.
• Consider GPU and hardware support. Is your business equipped with what the software needs?
• Decide whether you want to use cloud-based deep learning platforms or host the software on-premise. In many cases, cloud-based offers advantages like scale and on-demand pricing.
• Consider integration with existing systems.
• Community and support: will you be able to get help from other users?
• Trial and experimentation – whenever possible, try out the software through free trials or demos.

Before making a final decision, make sure to read reviews and testimonials from other businesses that have used the software.

Review Methodology

To determine the top-rated deep learning software, we conducted extensive research to identify the best deep learning software that is currently popular and widely used in various industries. Our research process involved studying user reviews, expert opinions, and industry reports to gather insights into the performance, features, and user satisfaction of different software solutions.

We evaluated the available tools based on six key data points: usability and user interface, range and flexibility of algorithms, compatibility with popular deep learning frameworks, scalability and performance, availability of support and documentation, and overall user experience.

We then ranked the best deep learning software based on their performance against the criteria and user feedback.

Bottom Line: Best Deep Learning Software

The best deep learning software depends on your specific needs and preferences. We analyzed several popular and high-performing deep learning software, each with its strengths and limitations – no tool is perfect for every situation.

Still, there is likely a deep learning tool that is the best for your particular use case. If you need more than deep learning software, we also analyzed the top AI as a service companies and their offerings to expand your options beyond this immediate AI sector.

Also see: What is Artificial Intelligence?