There are some Python Libraries for Machine Learning and AI. They are listed below.
- PyTorch Geometric
- OpenFace Caffe
- OpenFace Model
The NumPy is amazing python library. It is a powerful and fast array-processing library for Python and used for scientific computing with Python. It is also used in many commercial products, such as Matlab, IDL, and Maple. Numpy is an open-source, free, and cross-platform available for every one. It was created by a University of California, Berkeley professor, Tim Donoho, which is available under the MIT license.
SciPy is a free and open-source Python module for numerical computing and its applications in science, engineering, and technology.
It includes a collection of Python modules for numerical computations and high-performance scientific and engineering algorithms.
It is a programming interface to the C/C++ Numerical Python extension, and an extensive collection of scientific functions.
SciPy is a versatile library that enables you to solve a wide range of scientific and engineering problems in a variety of areas, such as statistical, linear algebra, differential equations, optimization, and signal processing.
SciPy is designed to be used in both scientific and engineering applications, as well as in other fields such as statistics, machine learning, and data analysis.
The following is a list of some of the applications that can be used with SciPy:
- Statistical Analysis
- Linear Algebra
- Differential Equations
- Signal Processing
- Data Analysis
- Image Processing
- Machine Learning
- Data Visualization
- Scientific Computing
Matplotlib is an open-source Python library for publication quality graphics. It is a 2D plotting library integrated with a 2D drawing interface for the Python programming language.
It supports vector and raster graphics, animation, image and plot editing, file I/O, data analysis, and a large selection of popular plot types.
Pandas is an open-source Python library. It is used for data analysis and manipulation. It provides an object-oriented framework for dataframes, which are lists of homogeneous numerical or categorical values.
Scikit-Learn is an open-source machine learning software library written in Python. It is designed to handle all manner of problems involving machine learning and data mining, from linear and logistic regression to clustering and dimensional reduction.
TensorFlow is an open source software library. It is used for machine learning. TensorFlow is Google’s framework for numerical computation. It is the machine learning software library that powers Google’s search, speech, and other technologies.
PyTorch is an open-source deep learning framework for Python. It is developed by Facebook and aims to provide a unified deep learning platform for research and production.
A deep learning framework is an application programming interface (API) that allows you to use a specific deep learning algorithm in a specific way.
PyTorch aims to enable researchers and developers to use deep learning algorithms, such as Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in a way that’s easy to understand.
The PyTorch team has been working on the project since 2013. They released the first version of PyTorch in January 2015. Since then, the project has grown significantly.
Thanks to this growth, the PyTorch team has released a number of tools and libraries that can be used to speed up or ease the process of using deep learning algorithms.
PyTorch Geometric is a deep learning framework for Python that comes with various pre-built models and utilities. It aims to provide a unified deep learning platform for research and production.
OpenCV is a cross-platform Python library. It is a computer vision and machine learning library. It supports a variety of computer vision tasks, including object detection, tracking, motion estimation, and scene classification.
It is written in C, C++, and Python, and is widely used in computer vision and machine learning applications.
OpenFace is an open-source cross-platform machine learning library for face detection and recognition.
It includes a robust face detection engine, face tracking, face landmark localization, face matching, face recognition, and face verification, as well as a face alignment algorithm.
OpenFace Caffe is a Caffe-based deep learning framework for face detection and recognition. It includes a face detection engine, face tracking, face landmark localization, face matching, face recognition, and face verification, as well as a face alignment algorithm.
OpenFace Model is a deep learning framework for face recognition and verification. It includes a face detection engine, face tracking, face landmark localization, face matching, face recognition, and face verification, as well as a face alignment algorithm.
It is open source model, written in C++, and can be used to build a complete face recognition system.
Keras is a Python framework for developing and training deep learning models. It’s a simple and powerful deep learning library, with a focus on ease of use and rapid prototyping.
Keras is built on top of Theano, a Python library for fast numeric computing, and TensorFlow, Google’s machine learning library.
Keras is an open-source project, licensed under the Apache License 2.0. There are two primary use cases for Keras.
First, it can be used to quickly and easily build state of the art models for classification or regression problems.
Second, Keras is a good tool to quickly build simple neural networks, without needing to understand the inner-workings of deep learning.
The Keras library provides a number of high-level building blocks for creating neural networks. For example, it provides a deep learning model class, which is a generalization of the neural network layers defined in other libraries such as Theano, Torch, and Caffe.
It also provides a very simple application programming interface (API) to build neural networks, as well as tools for data augmentation, pre-processing, and visualization.
The Keras library is easy to use. For example, a user doesn’t need to worry about how to set up a model. Instead, the user can focus on developing the model’s architecture and training the model.