Catel et Bocquet retracent le destin de la fascinante Joséphine Baker dans un magnifique roman (bio)graphique tout en noir et blanc. En 500 pages, les auteurs nous dévoilent toutes les facettes de cette femme emplie de convictions : muse de nombreux artistes, militante contre la ségrégation raciale, agent du contre-espionnage de la France Libre, mère adoptive d’une douzaine d’enfants venus d’horizons divers… elle était décidément bien plus qu’une danseuse de cabaret affublée d’une ceinture de bananes...
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability-and understand how and when they're used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Joel Grus is a software engineer at Google. Before that he worked as a data scientist at multiple startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com.