论文标题
Python中的机器学习：数据科学，机器学习和人工智能的主要发展和技术趋势
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
论文作者
论文摘要
更智能的应用程序可以更好地利用从数据中收集的见解，对每个行业和研究学科产生影响。这次革命的核心是工具和驱动它的方法，从处理每天生成的大量数据到学习和采取有用的行动。深层神经网络以及经典ML和可扩展的通用GPU计算的进步已成为人工智能的关键组成部分，从而使许多令人震惊的突破都可以降低采用障碍。 Python仍然是科学计算，数据科学和机器学习最喜欢的语言，通过启用低级库和清洁高级API来提高性能和生产力。这项调查提供了对Python的机器学习领域的见解，通过重要主题进行了巡回演出，以确定一些启用了它的核心硬件和软件范式。我们介绍了广泛使用的库和概念，共同收集了整体比较，目的是教育读者并推动Python Machine学习的领域。
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.
