AI frameworks are software tools that provide developers with a set of modular components to build and deploy AI-based applications. The main purpose of these frameworks is to provide an efficient and effective way to build AI applications. They are designed to provide a wide range of features and capabilities including deep learning, natural language processing, computer vision, and machine learning. These frameworks also provide developers with an easy-to-use platform that simplifies the development process and allows for rapid deployment of AI applications. Popular AI frameworks include TensorFlow, PyTorch, Caffe, Theano, MXNet, and Chainer.
Scikit-learn is a free open-source machine learning library for the Python programming language. It features various algorithms, including supervised and unsupervised learning, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. It is licensed…
PyTorch is an open-source deep-learning platform that provides a seamless path from research prototyping to production deployment. It provides an end-to-end research platform, with seamless integration of research prototyping and production deployment. PyTorch provides strong support for both imperative and…
TensorFlow is an open-source machine learning framework developed by Google Brain Team and released under the Apache 2.0 license. It is an end-to-end platform for developing and deploying deep learning models. TensorFlow has become the most popular AI framework for…