Priori to the explosion of AI frameworks, the following are several AI frameworks that were widely used in application development. These provide pre-built functionalities and structures that make it easier for developers to incorporate AI into their applications:
TensorFlow is an open-source library developed by Google Brain Team. It's used for both research and production at Google. TensorFlow is particularly good at training and inference of deep learning models. It provides a flexible and comprehensive ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
PyTorch is a Python-based scientific computing package targeted at two sets of audiences: a replacement for NumPy to use the power of GPUs, and a deep learning research platform that provides maximum flexibility and speed. PyTorch is well-loved for its dynamic computational graph and efficient memory usage, and it's maintained by Facebook's AI Research lab.
Keras is an open-source neural network library written in Python. It's capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Keras is known for its user-friendliness, modularity, and ease of extending and connecting with other Python libraries.
Scikit-learn is a machine learning library for Python built on SciPy, NumPy, and Matplotlib. It's open-source and commercially usable. This library provides a selection of efficient tools for machine learning and statistical modeling, including classification, regression, clustering, and dimensionality reduction via a consistent interface. It's known for its clear API and extensive documentation.
5. Microsoft Cognitive Toolkit (CNTK):
The Microsoft Cognitive Toolkit (CNTK) is a unified deep-learning toolkit by Microsoft Research. It describes neural networks as a series of computational steps via a directed graph and allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs).
6. Apache MXNet:
Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. It provides support for multiple languages including Python, R, Scala, and C++, and it allows you to design, train, and deploy deep neural networks on a variety of platforms including cloud services.
These are just some of the popular AI frameworks used in application development. The choice of framework depends on the specific needs of the project, such as the type of model being used, the need for scalability, and the preferences of the development team.