His Linkedin Profile: linkedin. That's where you would find this book useful. Table of Contents1. Yashavant Kanetkar has created, molded and groomed lacs of IT careers in the last three decades. His LinkedIn Profile: linkedin. Aditya was formerly at Oracle America Inc. The second edition made it even better. Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library.
You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. We've gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion.
Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.
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.
No previous experience with Keras, TensorFlow, or machine learning is required. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework.
He also does AI research, with a focus on abstraction and reasoning. The book assumes basic knowledge of Python. Experienced Python developers who want to learn about geospatial concepts, work with geospatial data, solve spatial problems, and build map-based applications. It will teach you how to create visuals, do event handling, create 3D games, add media elements, and integrate OpenGL into your Python game.
In this update to the first ever book to cover the popular open source PyGame games development library, you'll stand to gain valuable technical insights and follow along with the creation of a real-world, freely downloadable video game. Written by industry veterans and Python experts Will McGugan and Harrison Kinsley, this is a comprehensive, practical introduction to games development in Python. You can also capitalize upon numerous tips and tricks the authors have accumulated over their careers creating games for some of the world's largest game developers.
Guttag Publisher: MIT Press ISBN: Category: Computers Page: View: The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.
This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data.
This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms.
Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform and misinform as well as two related but relatively advanced topics: optimization problems and dynamic programming.
This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics. When it comes to creating powerful and effective hacking tools, Python is the language of choice for most security analysts.
The second edition of this bestselling hacking book contains code updated for the latest version of Python 3, as well as new techniques that reflect current industry best practices. Learn how with the second edition of Black Hat Python. New to this edition: All Python code has been updated to cover Python 3 and includes updated libraries used in current Python applications.
Additionally, there are more in-depth explanations of the code and the programming techniques have been updated to current, common tactics. Examples of new material that you'll learn include how to sniff network traffic, evade anti-virus software, brute-force web applications, and set up a command-and-control C2 system using GitHub. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.
The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others.
By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. This book is also an excellent resource for software developers who wish to use Linux as a developer workstation. Your email address will not be published.
Save my name, email, and website in this browser for the next time I comment. How to Visualize Data with D3 [Video]. How to Visualize Data with R [Video]. PC Magazine March Architecture 6. Microarchitecture: With contributions from Matthew Watkins 7. He received his Ph. Before attending Stanford, he worked at Intel as a logic and circuit designer on the Itanium and Pentium II processors. Since then, he has consulted at Sun Microsystems, Hewlett-Packard, Broadcom, and other design companies.
David holds more than a dozen patents and is the author of three other textbooks on chip design, as well as many Southern California hiking guidebooks. When he is not working, he enjoys hiking, flying, and making things with his three sons. Sarah Harris Sarah L.
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