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Neural network software enables the implementation, deployment and training of artificial neural networks. These networks are designed to mimic the behavior of the human brain and are used for a wide variety of tasks, including pattern recognition, data analysis, and prediction.
While there are hundreds of neural network software applications (free and paid), it can get overwhelming when shopping for the best option for your organization. We did the heavy lifting for you by selecting the best neural network software.
Here are our 10 top picks for the best neural network software — plus an additional 10 honorable mentions down below.
TABLE OF CONTENTS
Top Neural Network Software: Comparison Chart
Here is a head-to-head summary of the best neural network software features and pricing.
| Best for | High-level API | Community support | Primary language | Starting price | |
|---|---|---|---|---|---|
| Keras | Rapid prototyping | Yes (built on) | High | Python | Free |
| TensorFlow | Production deployment | Yes (built on) | High | Python | Free |
| PyTorch | Modularity and quick experimentation | Yes | High | Python | Free |
| Apache MXNet | Flexible research prototyping | Yes | Moderate | C++ | Free |
| Torch | Researchers and developers in the academic and research community | No | Low | Lua | Free |
| Weka | Developing new machine learning schemes | No | Low | Java | Free |
| Neural Designer | GUI-based development | Yes | Low | C++ | $2.495 per user per year |
| Chainer | Small to medium-sized projects | No | Moderate | Python | Available upon request |
| Caffe | Image classification and computer vision tasks | No | Moderate | C++ | BSD 2-Clause license |
| Knet | Dynamic computation | No | Low | Julia | Free |
Top Neural Network Software
Keras: Best for Rapid Prototyping
Keras is a high-level, open-source neural network library written in Python. It can run on top of other deep learning frameworks, such as TensorFlow, Theano or CNTK, giving you a simplified and intuitive API to define and run neural networks. It supports various types of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their combinations.
Pros and Cons
| Pros | Cons |
|---|---|
| You can serve Keras models via a web API. | Some users reported that Keras has limited customization capability. |
| Organizations like CERN, NASA and NIH use it. | According to some users, initial setup on Windows OS is a bit challenging. |
| Extensive documentation. | |
| Limited learning curve. |
Pricing
Keras is a free, open-source tool.
Features
- Keras models can be deployed to run directly in the browser or TF Lite to enable it to run on iOS, Android and embedded devices.
- High-performance backend engines.
- Pre-trained models.
- Built-in data preprocessing utilities.
TensorFlow: Best for Production Deployment
Released in 2015, TensorFlow is an end-to-end framework for machine learning developed by Google to enable you to prepare data, build, and deploy ML models and implement MLOps. TensorFlow allows users to develop and deploy neural networks, perform numerical computations, and train models across different platforms. It is widely used for various applications, such as image recognition, natural language processing, computer vision, and reinforcement learning. The software is deployable on the web, mobile, edge, and servers.
Pros and Cons
| Pros | Cons |
|---|---|
| Extensive community. | Resource-intensive. |
| Tensorflow supports Keras. | A steep learning curve for beginners due to its complexity. |
| It works well when processing image, text, and audio data. | |
| Tensorflow is highly scalable. |
Pricing
TensorFlow is free and open-source software.
Features
- While TensorFlow is primarily implemented in Python, it provides bindings for other languages, such as C++, Java, JavaScript, and Go.
- TensorFlow has a large and active community of researchers, developers, and data scientists.
- TensorFlow includes TensorBoard, a visualization tool that helps users understand, debug, and optimize the performance of their machine-learning models.
- Contains pre-trained models and datasets.
PyTorch: Best for Modularity and Quick Experimentation
Developed by Facebook’s AI Research (FAIR) group – now META AI – PyTorch is another popular open-source machine learning library for developing and training neural network-based deep learning models. Unlike frameworks like TensorFlow, which uses static computation graphs, it provides a dynamic computational framework, allowing artificial intelligence developers to define and run computational graphs on the fly, which makes it highly flexible and efficient for deep learning tasks.
Pros and Cons
| Pros | Cons |
|---|---|
| Easy debugging and rapid prototyping. | Limited visualization tools. |
| Large and active community. | Some users report scalability issues. |
| Comprehensive documentation. |
Pricing
PyTorch is free to install and use.
Features
- PyTorch is compatible with major cloud services such as AWS, GCP, Alibaba Cloud, and Azure.
- It offers a rich set of libraries and tools, such as Captum, PyTorch Geometric, and skorch, to support your development process in NLP and computer vision.
- Dynamic computational graph.
- It’s well supported by major cloud platforms.
Apache MXNet: Best for Flexible Research Prototyping
Apache MXNet is an open-source project that provides a deep learning framework for training and deploying deep neural networks on various devices, from cloud infrastructure to mobile devices. One of the key features of MXNet is its dynamic computational graph, which allows for efficient memory usage and flexible model architectures. It also provides a wealth of pre-built neural network layers and algorithms, as well as support for popular deep learning frameworks such as Gluon, Keras, and TensorFlow.
Pros and Cons
| Pros | Cons |
|---|---|
| It offers auto differentiation to derive gradients. | Limited update due to its small community. |
| You can use it for research projects on subjects like deep fake detection, self-driving cars, fraud detection, and even natural language processing applications. | MXNet’s ecosystem and tooling may not be as vast as some other deep-learning frameworks. |
| The addition of Gluon API enables developers to define dynamic neural network models. | |
| It supports multiple languages. |
Pricing
MXNet is a free, open-source tool.
Features
- Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R, and Perl.
- Supports various use cases, including computer vision, NLP, and time series.
- MXNet supports imperative programming using NDArray, making it useful for storing and transforming data, much like NumPy’s ndarray.
- Key-value store interface for efficient parameter synchronization.
Torch: Best for Researchers and Developers in the Academic and Research Community
Built on Lua is a lightweight and embeddable scripting language offering support for multiple programming methods, including procedural, object-oriented, functional, and data-driven programming. Torch is known for its speed and high-performance capabilities, making it popular among researchers and practitioners in the field of deep learning. It has a large community of developers contributing to its development and use.
Pros and Cons
| Pros | Cons |
|---|---|
| Users reported that they found the abstraction of Torch’s APIs very helpful. | Complex initial setup. |
| Easy to use, even for beginners. | Smaller user base compared to mainstream frameworks. |
| It has many packages in machine learning, signal processing, audio, video, and parallel processing. |
Pricing
Torch is a free, open-source tool.
Features
- Numeric optimization routines.
- Embeddable, with ports to iOS and Android backends.
- Neural network and energy-based models.
- Offers routines for indexing, slicing, and transposing.
Weka: Best for Developing New Machine Learning Schemes
Weka (an acronym for Waikato Environment for Knowledge Analysis) is open-source software issued under the GNU General Public License. It provides a collection of algorithms, tools, and libraries for predictive modeling, data preprocessing, classification, regression, clustering, and visualization. The Weka software provides several neural network algorithms for training and testing neural network models, such as multilayer perceptron, radial basis function network, and RProp, among others.
Pros and Cons
| Pros | Cons |
|---|---|
| Its graphical user interface makes it easy to use. | Java dependency. |
| Offers experimentation environment. | The user interface can be improved. |
| Suitable for initial data exploration and understanding. |
Pricing
Weka is freely available under the GNU General Public License.
Features
- Integration capabilities.
- Data preprocessing capabilities.
- Visualization.
- Comprehensive algorithms.
Neural Designer: Best for GUI-Based Development
Neural Designer is commercial neural network software that uses artificial neural networks for data modeling and predictive analytics. It allows users to create, train, and deploy neural network models without the need for extensive knowledge of coding or machine learning algorithms.
Pros and Cons
| Pros | Cons |
|---|---|
| Rapid development. | Not a free tool. |
| Visualization capability. | Limited integration capability. |
| Offers automatic model selection and hyperparameter optimization. |
Pricing
Neural Designer offers standard license and academic license.
Standard licenses
- Free trial: 15 days.
- Small: $2.495 per user per year or $6.245 per user for lifetime access.
- Medium: $4.995 per user per year or $12.495 per user for lifetime access.
- Large: $7.495 per user per year or $18.745 per user for lifetime access.
Academic licenses
- Research: $2.495 per user per year or $6.245 per user for lifetime access.
- Teaching: $4.995 per user per year or $12.495 per user for lifetime access.
- Campus: Quote available upon request.
Features
- GPU acceleration (NVIDIA CUDA).
- Multi-core parallelization (Intel MKL).
- Desktop (Windows, macOS, Linux).
- Cloud (AWS BYOL).
Chainer: Best for Small to Medium-Sized Projects
Chainer is a fully featured neural network software that allows for easy and intuitive definition of complex neural network models. Chainer is written in Python and can be used with popular libraries such as NumPy for numerical computations. It is designed to be efficient and scalable, making it suitable for both research and production environments.
Pros and Cons
| Pros | Cons |
|---|---|
| Ease of debugging. | Beginners may experience steep learning curve. |
| Support GPU computation with CUDA. | Smaller user base. |
| Seamless integration with NumPy. |
Pricing
Available upon request.
Features
- It supports several network architectures, including recursive nets, convnets, feed-forward nets, and recurrent nets.
- Support NVIDIA cuTENSOR and CUB for better performance.
- Chainer has four extension libraries, including ChainerMN, ChainerRL, ChainerCV, and ChainerUI.
Caffe: Best for Image Classification and Computer Vision Tasks
Caffe was created by Yangqing Jia during his PhD at UC Berkeley. It’s a “deep learning framework made with expression, speed, and modularity in mind.” It allows researchers and developers to define, train, and deploy various types of deep learning models. Caffe gained popularity for its efficiency, scalability, and modularity, making it a popular choice in the field of computer vision.
Pros and Cons
| Pros | Cons |
|---|---|
| Users find Caffe fast, flexible, and scalable. | Limited documentation. |
| It runs on both GPU and non-GPU based system. | Users reported that the tool is not easy to install on Anaconda software. |
| Low learning curve. |
Pricing
Caffe is released under the BSD 2-Clause license.
Features
- Caffe can process over 60M images per day with a single NVIDIA K40 GPU.
- Backed by a strong and active community of developers and researchers.
- Supports CPU and GPU computation.
Knet: Best for Dynamic Computation
Knet (pronounced “kay-net”) is a deep learning framework implemented in the Julia programming language. It provides a high-level interface for building and training deep neural networks. It aims to provide both flexibility and performance, allowing users to build and train neural networks on CPUs or GPUs efficiently.
Pros and Cons
| Pros | Cons |
|---|---|
| High performance on both CPUs and GPUs. | Limited documentation. |
| Research-oriented. | If you are not familiar with the Julia programming language, there may be a learning curve associated with using Knet. |
| Support for automatic differentiation. |
Pricing
Knet is free, open-source software.
Features
- Integration with Julia.
- Knet machine images are available for AWS, Singularity, and Docker.
- Uses dynamic computational graphs.
Honourable Mentions
Aside from the neural network software mentioned above, below are other top neural network software worth mentioning.
- Merlin – Best for neural network-based speech synthesis.
- Clarifai – Ideal for computer vision-related tasks.
- TFLearn – Well-suited for quick TensorFlow model building.
- DeepPy – Good for Pythonic programming based on NumPy’s ndarray.
- Bitnami Pytorch – Best for GPU acceleration.
- ConvNetJS – Good for training deep learning models like neural networks in web browsers.
- Scikit-learn – Good for predictive data analysis.
- Neuroph – Best for training neural networks in Java programs.
- NeuroSolutions – Good for cluster analysis.
- Darknet – Best for deep computation and image classification.
Key Features of Neural Network Software
Integration with Hardware Accelerators
Compatibility with hardware accelerators (e.g., GPUs, TPUs) to speed up the training and inference processes. This is particularly important for large-scale neural network applications.
Comprehensive Documentation
Neural network software are highly technical and may require you to invest time and effort in understanding the concepts and functionalities.
Here are some documentation checklists to use:
- Installation guide.
- User guide.
- API documentation.
- Examples and tutorials.
- Troubleshooting guide.
- Support and community resources.
Data Handling and Preprocessing
Neural network software should have capabilities for pre-processing and engineering features from raw data. This includes tasks such as data normalization, outlier removal, and feature scaling.
Integration with Other Libraries and Frameworks
Neural network software should have the ability to integrate with other popular libraries and frameworks. This allows users to leverage the functionalities and resources provided by these libraries for tasks such as data manipulation, visualization, and parallel computing.
How to Choose the Best Neural Network Software for Your Business
When shopping for the best neural network software for your business, you must first evaluate your organization to assess your specific needs and the tasks you want to carry out with neural network software. Keep in mind that not all neural network software functions the same way; some may be easy to use but lack advanced features, while others may offer advanced features but have a steeper learning curve.
Our analysis found that Caffe is ideal for computer vision related tasks, while Keras is well-suited for prototyping. Those with limited computer programming knowledge may find Neural Designer beneficial. On the other hand, Chainer is a good option for running small to medium-sized projects.
Be sure to research available software and consider factors such as ease of use, flexibility, scalability, available resources (documentation, community support, tutorials), and compatibility with your existing technology stack before settling for a particular neural network tool. Though neural network software usually supports multiple programming languages such as Python, R, and C++, consider the programming language native to your organization and choose software that offers strong support for that language.
Frequently Asked Questions (FAQs)
We answered the most commonly asked questions about neural network software.
What is the best neural network software?
The best neural network for your business is the one that offers the features, capabilities, and functionalities you need. Our top picks offer extensive community support, active development, and good performance across various tasks.
What are the common applications of neural network software?
Neural network software has a wide range of applications, including:
- Image and speech recognition.
- Natural language processing.
- Sentiment analysis.
- Financial analysis.
- Fraud detection.
- Recommendation systems.
Can neural network software run on a regular computer?
Yes, neural network software can run on regular computers, but the performance may vary depending on the network’s complexity and the size of the dataset.
Bottom Line: Best Neural Network Software
As stated earlier in this article, the best neural network software for your enterprise depends on your specific requirements and tasks. Some software may specialize in image recognition, while others may be more suitable for natural language processing or time series analysis. Hence, choosing a tool that aligns with your business objectives is important.
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