Machine Learning in Python: Main developments and technology trends in\n data science, machine learning, and artificial intelligence

Smarter applications are making better use of the insights gleaned from data,\nhaving an impact on every industry and research discipline. At the core of this\nrevolution lies the tools and the methods that are driving it, from processing\nthe massive piles of data generated each day to learning from and taking useful\naction. Deep neural networks, along with advancements in classical ML and\nscalable general-purpose GPU computing, have become critical components of\nartificial intelligence, enabling many of these astounding breakthroughs and\nlowering the barrier to adoption. Python continues to be the most preferred\nlanguage for scientific computing, data science, and machine learning, boosting\nboth performance and productivity by enabling the use of low-level libraries\nand clean high-level APIs. This survey offers insight into the field of machine\nlearning with Python, taking a tour through important topics to identify some\nof the core hardware and software paradigms that have enabled it. We cover\nwidely-used libraries and concepts, collected together for holistic comparison,\nwith the goal of educating the reader and driving the field of Python machine\nlearning forward.\n