Here are the top AI projects with Python that are being used by AI professionals around the world.

Artificial intelligence has been one of the advanced topics in the tech industry. The implementation of AI applications is growing rapidly, and tech enthusiasts have to keep up with this evolving field to work with AI-driven tools and applications. One of the most popular programming languages that are implemented on AI and ML projects is Python. This article provides a list of open-source AI projects and applications with Python.

• TensorFlow: It lists as one of the top open-source AI projects with Python. TensorFlow is a product of Google and helps developers in creating and training ML models. It has helped ML engineers convert prototypes into working materials quickly and efficiently. Currently, it has thousands of users worldwide and is a go-to solution for AI

• Chainer: Chainer is a Python-based framework to work on neural networks. It supports multiple network architectures simultaneously, including recurrent nets, recursive nets, and feed-forward nets. Also, it allows CUDA computation so that the users can use GPU with very few lines of code.

• PyTorch: PyTorch helps in research prototyping so that the users can deploy the products faster. It permits transmission between graph modes through TorchScript and provides distributed training that the users can scale. This model is available on multiple cloud platforms and has numerous tools in its ecosystem to support NLP, computer vision, and other solutions.

• Shogun: It is a machine learning library and assists in creating efficient ML models. Shogun is not based on Python exclusively as it can be used with several other programming languages like C#, Lua, Ruby, and R, to name a few. It allows combining several algorithm classes and data presentations so that users can prototype data pipelines quickly.

• Gensim: It is an open-source Python library that can analyze plain text files for a deeper understanding of the semantic structures, and also retrieve semantically similar files, and perform such other tasks. Like any other Python library, it is scalable and platform-independent.

• Statsmodels: It is a Python module that provides classes and functions for the estimation of different statistical models, for conducting tests, and for statistical data exploration. It supports specifying models using R-style formulas and data frames.

• Theano: Theano allows users to evaluate mathematical operations including multi-dimensional arrays efficiently. It is used in building deep learning projects. Theano's high speeds give tough competition to the C implementations for problems involving large amounts of data. It is programmed to take structures and convert them into efficient codes.

• Keras: Keras is an accessible API for neural networks. It is based on Python and can also run on CNTK, TensorFlow, and Theano. It is written using Python and follows the best practices to reduce cognitive pressure. It makes working on deep learning projects more efficient.

• NuPIC: It is an open-source project based on the theory of HTM (Hierarchical Temporal Memory). Its deep experience in theoretical neuroscience research has led to tremendous discoveries about how the brain works. Its deep learning systems have demonstrated impressive achievements.

• Scikit-learn: It is a Python-based library of tools and applications that can be used for data mining and data analysis. This tool has excellent accessibility and is extremely easy to use. The developers have built it on NumPy and SciPy to facilitate efficiency for beginners and intermediates.