What is Machine Learning? 18 Crucial Concepts in AI, ML, and LLMs
20 Best Neural Network Software Today
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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.
Top Neural Network Software: Comparison ChartHere 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 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. PricingKeras is a free, open-source tool.
Features
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. PricingTensorFlow is free and open-source software.
Features
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. PricingPyTorch is free to install and use.
Features
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. PricingMXNet is a free, open-source tool.
Features
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. PricingTorch is a free, open-source tool.
Features
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. PricingWeka is freely available under the GNU General Public License.
Features
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. PricingNeural Designer offers standard license and academic license.
Standard licenses
Academic licenses
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. PricingAvailable upon request.
FeaturesCaffe 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. PricingCaffe is released under the BSD 2-Clause license.
FeaturesKnet (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. PricingKnet is free, open-source software.
FeaturesAside from the neural network software mentioned above, below are other top neural network software worth mentioning.
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 DocumentationNeural 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:
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 FrameworksNeural 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 BusinessWhen 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:
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 SoftwareAs 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.
Read next: Best Artificial Intelligence Software
Meta's New Learning Algorithm Can Teach AI To Multi-task
If you can recognize a dog by sight, then you can probably recognize a dog when it is described to you in words. Not so for today's artificial intelligence. Deep neural networks have become very good at identifying objects in photos and conversing in natural language, but not at the same time: there are AI models that excel at one or the other, but not both.
Part of the problem is that these models learn different skills using different techniques. This is a major obstacle for the development of more general-purpose AI, machines that can multi-task and adapt. It also means that advances in deep learning for one skill often do not transfer to others.
A team at Meta AI (previously Facebook AI Research) wants to change that. The researchers have developed a single algorithm that can be used to train a neural network to recognize images, text, or speech. The algorithm, called Data2vec, not only unifies the learning process but performs at least as well as existing techniques in all three skills. "We hope it will change the way people think about doing this type of work," says Michael Auli, a researcher at Meta AI.
The research builds on an approach known as self-supervised learning, in which neural networks learn to spot patterns in data sets by themselves, without being guided by labeled examples. This is how large language models like GPT-3 learn from vast bodies of unlabeled text scraped from the internet, and it has driven many of the recent advances in deep learning.
Auli and his colleagues at Meta AI had been working on self-supervised learning for speech recognition. But when they looked at what other researchers were doing with self-supervised learning for images and text, they realized that they were all using different techniques to chase the same goals.
Data2vec uses two neural networks, a student and a teacher. First, the teacher network is trained on images, text, or speech in the usual way, learning an internal representation of this data that allows it to predict what it is seeing when shown new examples. When it is shown a photo of a dog, it recognizes it as a dog.
The twist is that the student network is then trained to predict the internal representations of the teacher. In other words, it is trained not to guess that it is looking at a photo of a dog when shown a dog, but to guess what the teacher sees when shown that image.
Because the student does not try to guess the actual image or sentence but, rather, the teacher's representation of that image or sentence, the algorithm does not need to be tailored to a particular type of input.
Data2vec is part of a big trend in AI toward models that can learn to understand the world in more than one way. "It's a clever idea," says Ani Kembhavi at the Allen Institute for AI in Seattle, who works on vision and language. "It's a promising advance when it comes to generalized systems for learning."
An important caveat is that although the same learning algorithm can be used for different skills, it can only learn one skill at a time. Once it has learned to recognize images, it must start from scratch to learn to recognize speech. Giving an AI multiple skills at once is hard, but that's something the Meta AI team wants to look at next.
The researchers were surprised to find that their approach actually performed better than existing techniques at recognizing images and speech, and performed as well as leading language models on text understanding.
Mark Zuckerberg is already dreaming up potential metaverse applications. "This will all eventually get built into AR glasses with an AI assistant," he posted to Facebook today. "It could help you cook dinner, noticing if you miss an ingredient, prompting you to turn down the heat, or more complex tasks."
For Auli, the main takeaway is that researchers should step out of their silos. "Hey, you don't need to focus on one thing," he says. "If you have a good idea, it might actually help across the board."
What Are Convolutional Neural Networks?
What are convolutional neural networks in deep learning?Convolutional neural networks are used in computer vision tasks, which employ convolutional layers to extract features from input data.
Convolutional neural networks (CNNs) are a class of deep neural networks commonly used in computer vision tasks such as image and video recognition, object detection and image segmentation.
Neural networks are machine learning models consisting of interconnected nodes that process information to make decisions, while deep neural networks have multiple hidden layers that enable them to learn complex representations for various tasks. They both mimic the structure and function of the human brain. Computer vision is a field of artificial intelligence (AI) that focuses on enabling machines to interpret and understand visual data from the world.
Although image and video recognition involves classifying or recognizing objects, scenes or actions in photos or videos, object detection involves locating certain things inside an image or video. Image segmentation involves dividing an image into meaningful segments or regions for further analysis or manipulation.
CNNs use several convolutional layers to automatically extract features from input data. The input data is subjected to filters by the convolutional layers, with the feature maps produced passed into further processing layers. Convolutional layers are the building blocks of CNNs that perform the operation of filtering and feature extraction on input data.
Filtering is the process of convolving an image with a filter to extract features, while feature extraction is the process of identifying relevant patterns or features from the convolved images.
Pooling layers, which downsample the output of the convolutional layers to lower the computational cost and increase the network's capacity to generalize to new inputs, are frequently included in CNNs in addition to the convolutional layers. Additional typical layers include normalization layers, which help to lower overfitting and enhance the network's performance, and fully connected layers, which are utilized for classification or prediction tasks.
Many applications, such as facial recognition, self-driving cars, medical image analysis and natural language processing (NLP), have extensively used CNNs. They have also been used to achieve state-of-the-art results in image classification tasks, such as the ImageNet challenge.
How do convolutional neural networks work?Convolutional neural networks work by extracting features from input data through convolutional layers and learning to classify the input data through fully connected layers.
The steps involved in the working of convolutional neural networks include the following:
Here is an illustration of how CNN would categorize pictures of cats and dogs:
The CNN is trained using a set of labeled images, with the weights of the filters and fully connected layers adjusted during training to minimize the error between the predicted and actual labels. Once trained, the convolutional neural network can accurately classify new, unseen images of cats and dogs.
What are the types of convolutional neural networks?Several types of convolutional neural networks exist, including traditional CNNs, recurrent neural networks, fully convolutional networks and spatial transformer networks — among others.
Traditional CNNsTraditional CNNs, also known as "vanilla" CNNs, consist of a series of convolutional and pooling layers, followed by one or more fully connected layers. As mentioned, each convolutional layer in this network runs a series of convolutions with a collection of teachable filters to extract features from the input image.
The Lenet-5 architecture, one of the first effective CNNs for handwritten digit recognition, illustrates a conventional CNN. It has two sets of convolutional and pooling layers following two fully connected layers. CNNs' efficiency in image identification was proved by the Lenet-5 architecture, which also made them more widely used in computer vision tasks.
Recurrent neural networks (RNNs) are a type of neural network that can process sequential data by keeping track of the context of prior inputs. Recurrent neural networks can handle inputs of varying lengths and produce outputs dependent on the previous inputs, unlike typical feedforward neural networks, which only process input data in a fixed order.
For instance, RNNs can be utilized in NLP activities like text generation or language translation. A recurrent neural network can be trained on pairs of sentences in two different languages to learn to translate between the two.
The RNN processes sentences one at a time, producing an output sentence depending on the input sentence and the preceding output at each step. The RNN can produce correct translations even for complex texts since it keeps track of past inputs and outputs.
Fully convolutional networksFully convolutional networks (FCNs) are a type of neural network architecture commonly used in computer vision tasks such as image segmentation, object detection and image classification. FCNs can be trained end-to-end using backpropagation to categorize or segment images.
Backpropagation is a training algorithm that computes the gradients of the loss function with respect to the weights of a neural network. A machine learning model's ability to predict the anticipated output for a given input is measured by a loss function.
FCNs are solely based on convolutional layers, as they do not have any fully connected layers, making them more adaptable and computationally efficient than conventional convolutional neural networks. A network that accepts an input image and outputs the location and classification of objects within the image is an example of an FCN.
Spatial transformer networkA spatial transformer network (STN) is used in computer vision tasks to improve the spatial invariance of the features learned by the network. The ability of a neural network to recognize patterns or objects in an image independent of their geographical location, orientation or scale is known as spatial invariance.
A network that applies a learned spatial transformation to an input image before processing it further is an example of an STN. The transformation could be used to align objects within the image, correct for perspective distortion or perform other spatial changes to enhance the network's performance on a specific job.
A transformation refers to any operation that modifies an image in some way, such as rotating, scaling or cropping. Alignment refers to the process of ensuring that objects within an image are centered, oriented or positioned in a consistent and meaningful way.
When objects in an image appear skewed or deformed due to the angle or distance from which the image was taken, perspective distortion occurs. Applying several mathematical transformations to the image, such as affine transformations, can be used to correct for perspective distortion. Affine transformations preserve parallel lines and ratios of distances between points to correct for perspective distortion or other spatial changes in an image.
Spatial changes refer to any modifications to the spatial structure of an image, such as flipping, rotating or translating the image. These changes can augment the training data or address specific challenges in the task, such as lighting, contrast or background variations.
What are the advantages of CNNs?CNNs are preferred in computer vision tasks due to their advantages, including translation invariance, parameter sharing, hierarchical representations, resilience to changes and end-to-end training.
Convolutional neural networks have several advantages, making them an attractive choice for various computer vision tasks. One of their main advantages is translation invariance, a feature of CNNs that allows them to recognize objects in an image regardless of their position. Convolutional layers are used to accomplish this by applying filters to the full input image so that the network can learn features that are translation-invariant.
The use of parameter sharing, in which the same set of parameters is shared across all areas of the input image, is another benefit of CNNs. As a result, the network has fewer parameters and can better generalize new data, which is crucial when working with huge data sets.
CNNs can also learn hierarchical representations of the input image, with the upper layers learning more complicated features like object pieces and forms, while the lower layers learn simpler elements like edges and textures. For challenging tasks like object detection and segmentation, this hierarchical model enables the network to learn characteristics at many levels of abstraction.
CNNs are suitable for real-world applications because they are resilient to changes in lighting, color and tiny distortions in the input image. Finally, convolutional neural networks can be trained end-to-end, allowing gradient descent to simultaneously optimize all of the network's parameters for performance and faster convergence. Gradient descent is an optimization algorithm used to iteratively adjust model parameters by minimizing the loss function in the direction of the negative gradient.
What are the disadvantages of CNNs?CNNs have drawbacks such as lengthy training time, the need for large labeled data sets and susceptibility to overfitting. Network complexity can also affect performance. However, CNNs remain a widely used and effective tool in computer vision, including object detection and segmentation, despite limitations in tasks requiring contextual knowledge like NLP.
Convolutional neural networks have several drawbacks that can make using them in some machine learning applications difficult. For instance, CNN training can take a while, especially for large data sets, because CNNs are computationally expensive. Furthermore, creating the CNN architecture can be challenging and necessitates a thorough comprehension of the fundamental ideas of artificial neural networks.
Another drawback is that CNNs need a lot of labeled data to train effectively. This can be a serious constraint in the circumstances with little data available. CNNs are also not always successful at tasks that require more contextual knowledge, such as NLP, even if they are quite good at image recognition tasks.
The number and kind of layers employed in CNN design can affect performance. For instance, adding more layers might improve accuracy but increase network complexity and computing costs at the same time. Deep learning CNN architectures are also vulnerable to overfitting, which happens when a network gets overly specialized to the training data and performs poorly on new, untrained data.
Despite these drawbacks, CNNs are still a widely used and very effective tool for deep learning and machine learning algorithms in the field of artificial neural networks, including segmentation, object detection and image recognition. That said, CNNs will remain a key player in computer vision.
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