Remember the biology test on neurons and the brain from your school days?
It’s time to revisit neural networks, but this time, it isn’t the human brain we’re discussing (or the results of that test). Instead, let’s talk about artificial neural networks.
Artificial neural networks (ANNs) mimic the human brain’s neural networks by learning using node layers. They are a subset of machine learning (ML), which helps machines learn and process information like the human brain.
Developers use artificial neural network software for deep learning, employing deep neural networks (DNNs) to build intelligent applications.
Despite their differences, artificial neural networks are generally compared against the human brain. However, they operate in a much more simplified manner.
The human brain contains billions of neurons that send and receive neurotransmitters and carry information throughout the body. These neurons help us think and learn by taking in sensory data as input, processing it, and providing an output. This continues until a desired outcome is attained through a highly interconnected network.
Artificial neural networks contain artificial neurons (called nodes) that help them learn from data and solve problems. The layers of the human brain inspire the algorithms. Like human brains, ANNs also have an interconnected network architecture that consists of three layers:
Upon receiving the information, the network processes it, decides what to do with it, and then shares its decision. Calculations occur as information passes from layer to layer through forward and backward propagation cycles as the network trains and learns. Here are some definitions to help understand the concept better:
Source: AI Mind
Different types of artificial neural networks meet varying levels of complexity and training needs.
A FNN is the simplest type of artificial neural network. Information flows in one direction from the input layer through the hidden layers and finally to the output layer, without any cycles or loops in the training process. Unless deemed necessary, the network may not even have a hidden layer due to its simplicity. There is no backpropagation involved in this method.
In a modular neural network, multiple independent networks work together to solve complex tasks by contributing their results to the collective output. This structure divides a larger task into smaller pieces, and a separate module handles each subtask accordingly. Throughout the process, modules operate independently, allowing each module to be trained separately on its respective data related to its assigned subtask. Networks don’t interfere with one another during the computation process.
Convolutional neural networks (sometimes referred to as ConvNets) are designed to process images and effectively recognize complex patterns. They extract features from data and learn through training on a given set of images. Due to their automated feature extraction capabilities, CNNs are highly effective for object classification (e.g., facial recognition) and other computer vision programs.
A recurrent neural network (RNN) processes and converts sequential data, such as words, sentences, and time-series information. Different types of RNN architectures exist, including one-to-many (one input to many outputs), many-to-many (many inputs to many outputs), and many-to-one (many inputs to one output). Recurrent neural networks work well for translations, text summarizations, and time series prediction models.
ANNs have transformed the field of artificial intelligence by mimicking the way the human brain processes information. Here are some key advantages of using ANNs:
While ANNs offer remarkable capabilities, they are not without challenges. Here are some key disadvantages of using ANNs:
ANNs are the basis for other deep learning algorithms, including the three applications below.
Facial recognition technology and biometrics use artificial neural networks to detect facial features and expressions, and distinguish faces from other nonface objects. Convolutional neural networks (CNNs), designed for image processing, including faces, excel in detecting complex features. They create a blueprint-like image known as a faceprint for reference in a database. Then, faces can be scanned and matched against the faceprint to verify identity. Security systems, social media platforms, and law enforcement use facial recognition technologies.
Tools like Google Translate use neural machine translation (NMT) to produce real-time translations from one language to another. In NMT, the network predicts the likelihood of a sequence of words by breaking down sentences into smaller parts to translate them. NMT then learns how to translate similarly structured sentences in the future.
Weather stations, satellite images, and radar trackers gather weather-related data, including temperature, humidity, barometric pressure, weather pattern imaging, precipitation tracking, and storm updates. These data sets can be used as input data for an artificial neural network (ANN). By training the ANN on historical weather data, it can learn to recognize patterns and relationships between variables for more accurate future weather predictions.
Artificial neural networks (ANN) software helps businesses gather data pools for baseline learning and model training. Developers also use deep neural networks (DNNs), a type of ANN, to build intelligent applications with deep learning functionality. These networks form the foundation for other deep learning algorithms, such as image recognition, voice recognition, and natural language processing (NLP).
To qualify for inclusion in the Artificial Neural Networks category, a product must:
* Below are the top five leading artificial neural network platforms from G2’s Winter 2024 Grid® Report. Some reviews may be edited for clarity.
Google Cloud Deep Learning VM Image provides preconfigured VMs for deep learning applications, making it easy and fast to start a project with a VM image that includes the most popular AI frameworks. Deep Learning VM Image supports machine learning frameworks like PyTorch and TensorFlow. Moreover, they are optimized with the latest NVIDIA® CUDA-X AI libraries and provide integrated support for JupyterLab.
“Nice tool for data scientists/ML engineers. It has all the latest modules and is easy to install. Pre-installed common dependencies and driver support for GPU/TPU and CLI options make it awesome. It makes possible a few hours of work done in a few clicks.”
- Google Cloud Deep Learning VM Image Review, Ramakant S.
“The user interface of the platform can be improved.”
- Google Cloud Deep Learning VM Image Review, Udit S.
AIToolbox contains AI modules written in Swift, including Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians, and Logistic Regression. Neural networks can support multiple layers, online and batch training, and feed-forward or simple recurrent layers mixed in simple network training.
“AIToolbox offers many tutorials, articles, and guides that help learn about new technologies. AIToolbox provides access to various AI and ML tools and libraries, making it easier for users to implement and experiment with new technologies. AIToolbox is designed to be user friendly with features that make it easy for users to access and use the platform."
- AIToolbox Review, Hem G.
“Sometimes it's lagging, and we are facing delay in the response; it's frustrating sometimes.”
- AIToolbox Review, Darshan S.
PyTorch is an open-source deep-learning framework that simplifies the development of ANNs. It offers dynamic computation graphs, easy debugging, and GPU acceleration, making it ideal for tasks like image recognition and natural language processing. Because of its flexibility and performance, PyTorch is widely used in both research and industry.
"The core PyTorch documentation is excellent, providing comprehensive insights, but many auxiliary libraries and newer features suffer from limited or incomplete documentation. Additionally, PyTorch's effectiveness diminishes when there isn't enough data to train the model, as the improvement and accuracy may not meet expectations."
- PyTorch Review, Alok Y.
"While PyTorch offers accessibility, it can be challenging for newcomers to the Python ecosystem. Deploying models beyond the training stage may require additional effort for a seamless transition. Furthermore, frequent updates, though demonstrating progress, can occasionally lead to compatibility issues that require attention and adaptation."
- PyTorch Review, Muneeb M.
Microsoft Cognitive Toolkit is an open-source deep learning toolkit for creating machine learning prediction models. Developers use it for speech recognition, image, and text training purposes with speed, accuracy, and commercial-grade quality. However, Microsoft has deprecated the framework, so it will no longer receive updates or further development.
“The most helpful features are easy navigation and low code for model creation. Any novice can easily understand the platform and create models easily. Support for various libraries for different languages makes it stand out! Great product compared to Google AutoML.”
- Microsoft Cognitive Toolkit (Formerly CNTK) Review, Anubhav I.
“Less control to customize the services to our requirements and buggy updates to CNTK SDKs, which sometimes breaks the production code.”
- Microsoft Cognitive Toolkit (Formerly CNTK) Review, Chinmay B.
node-fann is a fast, open-source neural network library for Node.js, based on the Fast Artificial Neural Network (FANN) library. It enables developers to build and train ANNs directly in a Node.js environment, providing an easy-to-use API for tasks such as pattern recognition, classification, and regression. Node-FANN supports various ANN architectures and training algorithms, offering flexibility for a range of machine-learning applications while being lightweight and efficient for integration into JavaScript-based projects.
"Node-FANN is easy to implement and integrate, with user-friendly documentation that stands out compared to other libraries. It offers cross-platform support, enabling dynamic usage across different systems. For beginners, pre-built examples are available, providing a life-saving resource for getting started. Additionally, cache optimization is included, enhancing the overall performance of the library."
- node-fann Review, Ritik S.
"A significant disadvantage is that it is not free. Additionally, I am not satisfied with the performance of Node FANN, a library designed for integrating networks into collaborative platforms. Its lack of documentation and examples makes it challenging for beginners to understand and effectively use."
-node-fann Review, Justin J.
ANNs are a groundbreaking advancement in the growing field of artificial intelligence. Inspired by the human brain, ANNs learn from data and perform complex tasks to the best of their technological capabilities.
Do you want to know more about machine learning models? Read more about them and how to train them.