Machine learning frameworks are software tools that provide pre-built libraries and modules for developing, training, and deploying machine learning models. These frameworks can help data scientists and developers streamline the process of building machine learning models, making it faster and more efficient to develop and deploy new models.

Figure 1. Machine Learning

Figure 1 shows machine learning. Here are some of the most popular machine learning frameworks:

TensorFlow:

TensorFlow is an open-source machine learning framework developed by Google. It is widely used for building deep learning models, including neural networks. TensorFlow supports a range of platforms, including desktops, mobile devices, and cloud platforms.

PyTorch:

PyTorch is an open-source machine learning framework developed by Facebook. It is designed to be flexible and user-friendly, and is commonly used for building neural networks and deep learning models.

scikit-learn:

scikit-learn is an open-source machine learning library for Python. It is designed for general-purpose machine learning tasks, such as classification, regression, and clustering. David Cournapeau developed it. It is very popular because it can easily work with NumPy and SciPy.

Keras:

Keras is an open-source neural network library written in Python. It is designed to be user-friendly and easy to use, and can run on top of TensorFlow or other backend frameworks.

Caffe:

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center. It is designed for image and video recognition tasks, and is widely used in computer vision research.

MXNet:

MXNet is an open-source deep learning framework developed by Apache. It is designed to be scalable and efficient, and supports a range of programming languages, including Python, R, and Julia.

Theano:

Theano is an open-source numerical computation library for Python. It is designed to be fast and efficient, and is commonly used for building deep learning models.

These are just a few of the many machine learning frameworks that are available today, and new frameworks are constantly emerging as the field of machine learning continues to evolve.

Cite this article:

Hana M (2023), Machine Learning Frameworks, AnaTechmaz, pp.59