20 Best Python AI and Machine Learning Open-Source Projects

AI and machine learning (ML) are advanced topics to learn. Python is the most popular language to use in AI and ML implementations. A good way to remain informed and educated about the advancements of ML is to interact with the community by contributing to the many open-source projects and resources that are used every day by advanced professionals. Here is the list of the top 20 Python open-source projects using AI and ML.

• Tensor Flow
• Keras
• Theano
• Scikit-learn
• Chainer
• Caffe
• Gensim
• PyTorch
• Shogun
• Pylearn2
• I learn
• Numenta
• PyMC
• DEAP
• Annoy
• Stats model
• Neon
• Orange 3
• Pybrain
• Fuel

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1) Tensor Flow

Researchers and engineers who worked at Google created TensorFlow. TensorFlow allowed them to rapidly and efficiently transform prototypes into working products. Abstraction is the single largest advantage TensorFlow offers for the advancement of machine learning. We can work remotely in the cloud, in the browser, or use it in on-premises applications.

2) Keras

Keras is a high-level neural networks API written in Python. It provides efficient execution of low-level CPU, GPU or TPU tensor operations. It offers simple extraction and building blocks for developing machine learning solutions. Its scalability and cross-platform capabilities help engineers.

3)Theano

Theano is a Python library used for numerical computation that can be run on the CPU or GPU. It was developed by the LISA group at the University of Montreal, Quebec, Canada. It is named after a Greek mathematician.

4) Scikit-learn

It is a Python-based library used for data analysis and data mining. It is commercially usable- BSD licensed and can be accessible to everybody, and reused in various contexts.

5) Chainer

For deep learning models, Chainer is a standalone open-source platform based on Python. It offers a versatile, intuitive and high-performance way for a wide range of deep learning models to be applied. The main advantage of Chainer is that it makes debugging the code very easy.

6) Caffe

It is produced by Berkeley AI Research and is a system for deep learning that focuses on modularity, speed and language. As it can process more than 60 million images a day, it has excellent architecture and speed. Besides, it has a flourishing community of developers who use it for industrial applications, multimedia, academic research and many other fields.

7) Gensim

Gensim is a free Python library with features such as scalable statistical semantics, semantic structure analysis of the plain-text document and semantically related documents. Gensim was mostly used for NLP projects.

8) PyTorch

PyTorch is a Python package that gives two high-level features: Tensor computation with strong GPU acceleration and deep neural networks built on a tape-based auto grad system. It provides maximum flexibility and speed. PyTorch is also available on several cloud platforms and has various libraries, resources supporting NLP, computer vision and many other solutions in its ecosystem.

9) Shogun

It is an open-source (machine learning library) and offers several unified and powerful ML methods. It is not strictly based on Python, but you can also use it in many other languages. It allows several algorithm types, data representations and tools to be combined so that data pipelines can be easily prototyped. It has a great testing infrastructure that you can use on different OS setups.

10) Pylearn2

Pylearn2 was initially developed by David Warde-Farley, Pascal Lamblin, Ian Goodfellow and others and is now developed by the LISA lab. Pylearn2 has more than seven thousand commitments on GitHub, and they are still rising, demonstrating its popularity among ML developers. It focuses on versatility and offers a broad range of features, including a media interface and a cross-platform interface.

11) Nilearn

Nilearn helps with data from Neuroimaging and is a common module for Python. It’s used to perform multiple statistical acts such as decoding, modeling, connectivity analysis and classification. Neuroimaging is a prominent field in the medical sector that can help to address various problems with greater precision such as better diagnosis.

12) Numenta

It is based on a neocortex theory called HTM (Hierarchical Temporal Memory). Solutions based on HTM and software have been developed by many people. HTM is a system for machine intelligence that relies on neuroscience.

13) PyMC

With algorithms such as the Markov Chain, PyMC uses Bayesian statistical models. It is a Python module and finds applications in many fields due to its versatility. For numeric problems, it uses NumPy and has a dedicated Gaussian process module.

14) DEAP

For exploring concepts and prototyping, DEAP is an evolutionary computing platform. With any sort of representation, you can work on genetic algorithms as well as perform genetic programming via prefix trees.

15) Annoy

Annoy stands for Approximate Nearest Neighbors. When using static files as indexes, it lets you perform closest neighbor searches. You can share an index across different processes with Annoy so that with each system you wouldn’t have to construct several indexes.

16) Stats Models

It is a Python module that allows users to perform statistical tests, estimate statistical models and explore data. Stats models are used for various types of data for estimation, a comprehensive list of descriptive statistics, statistical tests plotting function and result statistics are available.

17) Neon

Neon is a Python-based deep learning library for Nervana. Although offering the best efficiency, it offers ease of use.

18) Orange3

Orange3 for beginners and practitioners is open-source machine learning and data visualization. Interactive workflows for data preprocessing with a broad toolbox.

19) PyBrain

PyBrain is for Python, a modular Machine Learning Library. Its purpose is to include versatile, easy to use but still effective algorithms to evaluate and compare the algorithms for machine learning tasks and a variety of predefined environments.

20) Fuel

Fuel is a framework for the data pipeline that gives the need to the machine learning models. Both the Blocks and Pylearn2 neural network libraries to use it. Fuel enables various types of data (NumPy binary files, CSV files, HDF5 files) to be read quickly using a single interface based on Python’s iterator types.

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