Top 5 Neural Network Software Of 2023

Pop-up toasters, refrigerators dispensing ice through the door and voice-commanded electronics have one thing in common. They’re products of the human obsession with making interactive machines - and the further we go, the deeper our efforts in humanizing appliances and applications get. This endeavor has been enhanced by artificial intelligence (AI) and machine learning (ML) but how are they made?

Neural Network Software helps prepare artificial neural network (ANN) models which are based on biological neural networks, i.e., the neural network found in human brains. This is done by researching, simulating, developing and applying algorithms that can mimic how humans process, react and adapt to information and learn to make decisions the same way humans would.

Artificial Neural Networks require data pools to learn and with greater data comes greater learning, connections and more accurate and effective outputs. Deep Neural Networks (DNNs), which are made with additional hidden layers and are used for deep learning algorithms, offer functions of image recognition, voice recognition, natural language processing and more.

If your enterprise needs such a tool, check out our list of the top 5 Neural Network Software of 2023!
 

Top 5 Neural Network Software Of 2023

Neural Network Software is used to create and develop artificial neural networks that consist of algorithms that can imitate the behavior of human brains in the form of capturing, processing and reacting to information in the same way humans would.

So, let’s get into the list of the top 5 Neural Network Software for 2023 - in random order.
 

NVIDIA Deep Learning GPU Training System (DIGITS)	TensorFlow	Keras
Clarifai Enlight	Microsoft Cognitive Toolkit

 

Tool 1: NVIDIA Deep Learning GPU Training System (DIGITS)

NVIDIA is quite a familiar name across the technology sector, right? Yet, we’re going to give you a brief introduction to the company. It was founded in 1993 and has its headquarters in Santa Clara, California, USA. It has a workforce of around 25,000 employees across more than 50 offices worldwide. The company is listed on the Nasdaq and is among the Nasdaq -100 component and has been recognized among the 50 Smartest Companies by MIT Tech Review.


NVIDIA Deep Learning GPU Training System (DIGITS) offers a powerful platform of deep learning, that can be used to train effective and accurate deep neural networks (DNNs) for image classification, segmentation and object detection tasks. This is done by simplifying deep learning tasks such as managing data, designing and training neural networks, monitoring performance in real-time, selecting the best model and more. As an interactive platform, it saves time in programming and debugging, enabling you to focus on designing and training.

DIGITS is determined to progress your deep learning projects as effectively and efficiently as possible, which is why it’s an open-source project that lets you customize it to suit the needs of your application. It also lets you Integrate custom plug-ins and offers pre-trained models such as AlexNet, GoogLeNet, LeNet and UNET.

What You’ll Love: User knowledge repository and guides, User-friendly interface, Open source, Pre-existing templates, Hybrid cloud available.

Tool 2: TensorFlow

Google? Surely, you’ve heard of this name? The technology company was founded in 1998. Much later, in November 2015, the company released TensorFlow, its open-source framework for machine learning, which has become the most popular AI engine in use today. It’s no surprise that Google and its parent company, Alphabet Inc. lead in technology, especially looking at its portfolio of over 200 companies, which includes startups and acquisitions.


TensorFlow is an end-to-end machine learning platform that can help you prepare data, build and deploy ML models and implement MLOps. The tool can be used across pre-trained models or custom ones. It offers you deep learning, neural networks, general numerical computations and other statistical and predictive analytics workloads on CPUs, GPUs and clusters of GPUs, as well as Google’s own purpose-specific tensor processing units (TPUs).

TensorFlow is dedicated to making processes easier by allowing you to build and deploy models and algorithms through its common interface. Additionally, the tool can be used with a variety of programming languages, including Python, C++, JavaScript and Java. However, the coup de grace comes from its vast repository sources from an open-source community of developers, data scientists and data engineers.

What You’ll Love: User knowledge repository and guides, User-friendly interface, Open source, Pre-existing templates, Allows cross-platform integrations.

Tool 3: Keras

Keras was released in 2015 by creator François Chollet, who began working at Google later the same year. He is also a key contributor to the TensorFlow (yes, same as above) ML framework. It even bagged one of the top spots in the Stack Overflow Developers survey (2021) for being one of the most popular frameworks. Some of the companies that benefitted from the tool include NASA, CERN, US DOE, JP Morgan Chase, Orange, Comcast, YouTube, Netflix, Uber, Yelp, Instacart, Zocdoc, Twitter, Square/Block, and more.


Keras is a simple, flexible, powerful, end-to-end, open-source software library with a user-friendly, modular, and extensible interface to test and develop artificial neural networks. The platform was designed to allow users quick experimentation with deep neural networks. It helps by offering efficient execution of low-level tensor operations on CPU, GPU, or TPU (Google’s own purpose-specific tensor processing units) or on large clusters of GPUs.

Keras prides itself on being an API designed for human beings, not machines, in the sense that it follows a community-first philosophy. Keras is popular and admired by developers because it provides a wide range of tightly connected projects that cover every step of the ML workflow, through tools such as KerasNLP (Natural Language Processing), KerasCV (Computer Vision), KerasTuner (hyperparameter tuning), AutoKeras (rapid model prototyping) and more.

What You’ll Love: Easy to scale, User-friendly interface, Open source, Forecasting capabilities, Allows cross-platform integrations.

Tool 4: Clarifai Enlight

Clarifai was founded in 2013 and has its headquarters in Wilmington, Delaware, USA. The company has a workforce of around 100 employees in 3 offices and more spread across the world. Clarifai was recognized by G2 in 2022 as “Easiest Admin” for Spring, Summer, Fall and Winter and as a leader in the IDC MarketScape Computer Vision AI Software Platforms 2022.


Clarifai Enlight offers you a time and resource-saving API and user-friendly tool that lets you develop custom AI applications from scratch with highly accurate results with advanced knowledge transfer algorithms and neural network architecture. All this without prior experience in machine learning or extensive training data. Furthermore, Clarifai Models have been pre-built, pre-optimized and pre-trained with millions of expertly labeled inputs.

Clarifai Insight offers you tools with the fastest time-to-accuracy, which makes it easier and faster to evaluate the performance of your customized models by automating the testing and validation process, by also letting you predict against your own designs. This is done by generating random subsets of training data to test, after which a “5-split cross-validation” is performed while ensuring enough right examples are provided.

What You’ll Love: Customization possible, Easy to use, Chat/Email support available, Free trial available, Comprehensive dashboard.

Tool 5: Microsoft Cognitive Toolkit

Founded in 1975, Microsoft’s headquarters is in Washington, USA and its workforce exceeds 200,000 employees. While the multinational conglomerate enjoys a global presence of customers and offices, its flagship product, Windows, dominates the operating system market worldwide with over 76% share (as per Statistica - June 2022) and in 2023, the company was ranked #3 in the Fortune’s World’s Most Admired Companies, making it four years in a row.


Microsoft Cognitive Toolkit, AKA CNTK, offers a powerful open-source toolkit for exceptional distributed deep learning with high accuracy, efficiency and scalability. It can be used for applications like speech, vision, text, combinations and more. CNTK displays neural networks as a sequence of computations steps by making use of graphs, simplifying the process of realizing and combining popular model types like feed-forward Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs/LSTMs).

Microsoft CNTK is one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format, which is an open-source shared model representation for framework interoperability and shared optimization. What’s the big deal? ONNX enables developers to make use of frameworks such as CNTK, Caffe2, MXNet and PyTorch for models. Furthermore, CNTK provides libraries in Python, C++ C#/.NET and Java.

What You’ll Love: Outstanding performance, Suitable for startups, Open source, Advanced analytical capabilities, Allows cross-platform integrations.

Winding It Up

Transforming from an informative list to a simple decision could be tough but it can be made easy by evaluating from our list of top Neural Network Software. These tools help prepare artificial neural networks that generate outputs like how a living human would, by gathering, processing, learning, developing and applying algorithms that can provide accurate and effective solutions to users.

Remember, each company has unique needs, which is why you should ensure you pick a Neural Network Software that suits your requirements.

Until then, Autobots, roll out!