如果你是一名有经验的程序员，迅速阅读此书可以大体了解Python语言的核心。掌握了Python语言的核心，想再深入了解它的面向对象特性和编程技巧，可以看其他的Python大部头，或者最直接也是最有效的方式，下载并安装Python，在它的“Shell”里边用边学，这样可以事半功倍；如果你英语够好，python.org网站将是你挖宝的必经之地。此书也讲到了Python的这一易学特性，只要你仔细认真，定会从学习中得到乐趣。 　　《Python语言入门》曾是我大学时期读过的专业类好书之一，现在在我的同学中传阅。译者翻译得比较准确、通顺。在Python的入门级图书中，《Python语言入门》不失为一部经典之作。

Deep_Learning_for_Computer_Vision_with_Python，作者Adrian Rosebrock, 资料包含Starter, Practitioner, ImageNet Bundle三本书。

Deep Learning for Computer Vision with Python Practioner Bundle + Starter Bundle by Adrian Rosebrock of PyImageSearch

82篇顶会巨佬撰写的入门机器学习与深度学习的神书

Python Machine Learning By Example by Yuxi (Hayden) Liu English | 31 May 2017 | ASIN: B01MT7ATL5 | 254 Pages | AZW3 | 3.86 MB Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book Description Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. What you will learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds About the Author Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast. Table of Contents Getting Started with Python and Machine Learning Exploring the 20 newsgroups data set Spam email detection with Naive Bayes News topic classification with Support Vector Machine Click-through prediction with tree-based algorithms Click-through rate prediction with logistic regression Stock prices prediction with regression algorithms Best practices

Large Scale Machine Learning with Python [PDF + EPUB + CODE] Packt Publishing | August 4, 2016 | English | 439 pages Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

在本文中，我们开始创建自定义对象检测模型的过程。

总共1000多页，很好的资料，着重讲DL4J。

机器学习的信息理论基础。 我移除了里面添加的链接等信息，可以直接打印成很干净的书。