It is not an easy task to get into Machine Learning and AI. Given the enormous amount of resources that are available today, many aspiring professionals and enthusiasts find it hard to establish a proper path into the field. The field is evolving at a constant pace and it is crucial that we keep up with this rapid development.
In order to cope with the speed of evolution and innovation that is today so overwhelming, a good way to stay updated and knowledgeable on the advances that have taken place in ML is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals.
3. Keras: Keras that is written in Python is a high-level neural networks API, that is capable of running both on top of either TensorFlow or Theano. It was developed keeping its main focus on enabling fast experimentation.
5. Theano: Theano is a library written in Python that allows its users to define, optimize, as well as evaluate mathematical expressions that efficiently involve arrays that are multi-dimensional in nature. It can in order to perform efficient symbolic differentiation use GPUs.
6. Gensim: Gensim is a library in Python for document indexing topic modelling and similarity retrieval with large corpora. The Target audience is information retrieval (IR) and natural language processing (NLP) community.
7. Caffe: Caffe is a deep learning framework made with keeping in mind expression, speed, as well as modularity. The framework has been developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) as well as community contributors.
8. Chainer: Chainer is a deep learning framework framework that is Python-based and aims at flexibility. It based on the define-by-run approach (a.k.a. dynamic computational graphs) provides automatic differentiation APIs as well as object-oriented high-level APIs to build as well as train neural networks.
9. Statsmodels: Statsmodels is a package in Python that for statistical computations provides its users with a complement to Scipy that includes descriptive statistics, estimation as well as inference for statistical models.
10. Shogun: Shogun is Machine learning toolbox which provides its users with a wide range of unified as well as efficient Machine Learning (ML) methods. The toolbox very seamlessly allows the combination of multiple data representations, algorithm classes, as well as general purpose tools.