Top 5 Neural Network Software Of 2025

According to a report by McKinsey & Company, between 2025 to 2027, 92% of businesses will increase their AI investments. Yes, they are putting a lot of money into this technology!

Businesses and academics use Neural Network Software to make and use AI models that are smarter, faster, and more efficient. This is one of the key reasons why AI technologies are improving so quickly!

You see, Neural networks are the building blocks of machine learning. They work like the brain, processing data, finding trends, and making predictions. The right neural network platform is very important whether you're teaching AI to find anomalies in medical pictures, making personalized recommendation systems, or writing text that sounds like it was written by a person.

So, this begs the question: since there are a lot of tools out there, how do you pick the best one?

Well, that’s where we, TechDogs, come to the rescue!

We looked at the best Neural Network Software of 2025 to curate this list with the right tool for everyone, whether you're a company, a business, or just a fan of AI.

So, let's jump right in!
 

What Is A Neural Network Software?

First things first, a Neural Network is a computational model inspired by how the human brain processes information. It consists of layers of artificial neurons that work together to recognize patterns and learn from data. Specifically, Neural Network Software helps programmers create, train, and use AI models that work like the human brain for processing information. Deep learning also depends on these tools, allowing computers to find patterns, analyze data, and make smarter predictions.
 

Top 5 Neural Network Software Of 2025

So, we won’t keep you wondering any more. Explore the top 5 Neural Network Software tools of 2025 and discover which platform suits your AI ambitions best. Read on!
   

Tool 1: TensorFlow

Developed by the Google Brain team, TensorFlow is an open-source machine learning framework that has become a cornerstone in the AI community. Launched in 2015, it has evolved into a comprehensive platform for building and deploying machine learning models across various applications, from image and speech recognition to natural language processing and beyond. Recently, TensorFlow was also recognized with the “2024 Emotional Footprint Champion” award by SoftwareReviews. No wonder, it holds the title of most popular deep learning framework for business AI projects!
 

Why We Chose This Tool

TensorFlow has a strong ecosystem that can be leveraged by machine learning beginners and veterans alike. Its combination with Keras makes it easy for beginners to build neural networks, and more experienced users can use its low-level APIs to get more precise control. The framework allows many types of machine learning tasks, including supervised and unsupervised learning, along with diverse language support and integration options.
 
TensorFlow remains a leader in 2025, thanks to its new products like TensorFlow.js, which lets users run machine learning models right in the browser. Moreover, it boasts TensorFlow Lite which is designed for mobile and edge device deployments. These improvements make TensorFlow a top choice for a wide range of operational situations for neural networks.
 

What Users Like And Dislike

TensorFlow has loads of documentations, an active community, and powerful features that are always getting better with community contributions. Some users, though, have said that version compatibility issues during installation and the odd deprecation of code can be annoying. Overall, TensorFlow is still a popular choice for many because it is reliable, can be scaled up, and is still well known in business.
 

Tool 2: PyTorch

PyTorch, developed by Meta AI, is quickly becoming a favorite among researchers and writers for its flexibility and ease of use. A lot of people have adopted this open-source machine learning tool for neural network applications like computer vision and natural language processing. In 2024, PyTorch revealed that its deep learning framework saw a 63% adoption rate in the model training area, cementing its position as a leading Neural Network Software.
 

Why We Chose This Tool

PyTorch is unique as it has a dynamic computational graph that lets you change how networks behave in real time, which is very useful for ML development. As it is based on Python, it works well with other Python libraries like NumPy and Panda, simplifying adoption for both new and experienced coders.
 
PyTorch also has strong support for GPU acceleration, which speeds up the training process for neural networks that are very complicated. What’s more, PyTorch is supported across major cloud platforms, ensuring frictionless development and quick scaling of deployments.
 

What Users Like And Dislike

Users appreciate PyTorch's user-friendly syntax and dynamic graph capabilities for facilitating rapid prototyping and debugging. Its extensive community support and comprehensive documentation further enhance its appeal. However, some users note that deploying PyTorch models to mobile devices can be challenging as the framework lacks a built-in visual interface for model visualization. Overall, this Neural Network Software remains a fan favorite across universities, research institutions and organizations.
 

Tool 3: Keras

Keras is an open-source neural network library written in Python, designed to enable fast experimentation with deep neural networks. Developed by François Chollet and introduced in 2015, Keras has become a prominent tool in the neural network development community. It provides a user-friendly interface for building deep learning models and can run on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML.
 

Why We Chose This Tool

Keras is unique thanks to its API that is easy to use and understand. It also offers a high-level abstraction that makes it incredibly easy to build and experiment with neural networks. This design allows developers to focus on the core aspects of their ML models without getting bogged down by low-level implementation details.
 
Its versatility is another aspect that sets it apart, supporting both CPU and GPU computations, making it adaptable to various computing environments. Additionally, Keras works well with well-known deep learning backends like TensorFlow and Theano, giving you access to a huge library of pre-trained models and advanced features.
 

What Users Like And Dislike

People who use Keras like how simple and easy it is to use, making the development of neural networks simpler. The extensive documentation and supportive community are also highlighted as significant advantages. However, some users have reported challenges with debugging, mentioning that certain error messages can be difficult to interpret. Overall, Keras is still the best choice for software development teams that want to make their deep learning projects run more smoothly and make the quality of their work better.
 

Tool 4: JAX

Developed by Google, JAX is an open-source machine learning framework that streamlines the creation, training, and evaluation of neural networks. Celebrated for its performance and flexibility, JAX supports various neural network architectures, including feed-forward deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). In 2024, JAX received significant recognition within the deep learning community for its exceptional performance in tasks requiring rapid GPU or TPU execution.
 

Why We Chose This Tool

JAX stands out for its efficient utilization of computational resources, enabling rapid training across multiple GPUs and TPUs. Its design emphasizes flexibility, allowing users to combine different model types seamlessly. Additionally, JAX's functional programming approach facilitates composability and ease of debugging, highly valued abilities in the research and development of neural networks.
 
Its compatibility with Python enhances its adoption in diverse development environments. Additionally, JAX's optimized algorithms and efficient computation graph execution contribute to high performance, while its open-source nature fosters innovation. Moreover, JAX's high-level APIs simplify the development of neural network models, making it a versatile Neural Network Software across various applications.
 

What Users Like And Dislike

Users appreciate JAX's intuitive interface, comprehensive documentation, and ease of use. The tool's ability to handle complex neural network designs efficiently is also highly regarded. However, some users have noted that the community support is still growing compared to more established frameworks, making troubleshooting more challenging and time-taking. Overall, JAX is recognized for its solid performance and scalability in deep learning and neural network applications.
 

Tool 5: Caffe

Developed by the Berkeley Vision and Learning Center (BVLC), Caffe is an open-source deep learning framework renowned for its speed, modularity, and expressiveness. Initially released in 2014, Caffe has been widely adopted for image classification tasks due to its efficient processing capabilities. In fact, Caffe can process over 60 million images every day, powered by a NVIDIA K40 GPU and the ILSVRC2012-winning SuperVision model!
 

Why We Chose This Tool

Caffe's expressive architecture allows users to define models and optimization settings through its configuration files, eliminating the need for hard-coding. This design promotes flexibility and rapid experimentation, as the framework can seamlessly switch between CPU and GPU to facilitate efficient neural network training and deployment across various hardware setups.
 
Additionally, Caffe's Model Zoo offers a repository of pre-trained models, enabling users to leverage existing architectures for their specific applications. Caffe is also among the fastest convolution neural network implementations available on the market, boasting 1 ms/image for inference and 4 ms/image for training.
 

What Users Like And Dislike

Users like how quickly and effectively Caffe can handle image processing tasks while its modular design simplifies building and testing different neural network models. Although, some users find it hard to work with configuration files, especially for more complex models. Despite the steep learning curve for newcomers, over time, Caffe can provide a comprehensive framework for deep learning applications.
 

Conclusion

While these 5 Neural Network Software tools are cutting-edge and modern, there are some things you should consider. To choose the most suitable platform, think about the needs of your neural network tasks, computing resources, level of developers’ skill, etc.

Having the right Neural Network Software tool is important, whether you're working on a groundbreaking project, training large-scale machine learning models, or developing AI-powered apps.

Here’s a brief table to help you compare the top 5 Neural Network Software and pick the best one for your needs.

P.S: Always check the official websites of the vendors to see what's new, since AI systems change so quickly!
 

Starting Price	Free	Free	Free	Free	Free
Avg. User Rating	4.5	4.6	4.3	4.0	3.8
Best For	Versatile applications across industries	Research and development	Rapid prototyping	Large-scale deep learning tasks	Image classification tasks
Trail Period/Demo	NA	NA	NA	NA	NA
Top Customers	Google, Airbnb and Dropbox	Meta, Twitter and Salesforce	Netflix, Uber and Square	Google DeepMind, Nvidia and Apple	Yahoo, Adobe and Samsung