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Guidance Regarding Electronic Textbooks and Readings. Yashavant has been honored with the prestigious "e;Distinguished Alumnus Award"e; by IIT Kanpur for his entrepreneurial, professional and academic excellence. This award was given to top 50 alumni of IIT Kanpur who have made significant contribution towards their profession and betterment of society in the last 50 years. In recognition of his immense contribution to IT education in India, he has been awarded the "e;Best.
His Linkedin profile: linkedin. He has been designing distributed systems software for the last 4 years. He has worked at multiple companies in the past, including Oracle, Redfin, Amazon and Arista Networks. 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. Revised for pandas 1. Key Features This is the first book on pandas 1. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis.
This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations.
Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples.
Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book Description Python is one of the leading open source platforms for data science and numerical computing.
IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.
The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Some skills in mathematics will help you understand the theory behind the computational methods. Christmas, You'll learn how to analyze sales, understand customer data, predict outcomes, and present conclusions with clear visualizations.
You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. What you will learn Use Python's generator functions and generator expressions to work with collections in a non-strict or lazy manner Utilize Python library modules including itertools, functools, multiprocessing, and concurrent features to ensure efficient functional programs Use Python strings with object-oriented suffix notation and prefix notation Avoid stateful classes with families of tuples Design and implement decorators to create composite functions Use functions such as max , min , map , filter , and sorted Write higher-order functions Who this book is for This book is for Python developers who would like to perform Functional programming with Python.
Python Programming knowledge is assumed. You will be using popular libraries such as TensorFlow, Keras, sklearn, and so on to extend the functionalities of your financial applications by using smart machine learning techniques. 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. Requiring no prior knowledge of programming-related concepts, the book focuses on the easy-to-use, yet powerful, Python computer language.
This new edition is updated throughout to Python 3 and is designed not just to help scientists master the basics, but to do more in less time and in a reproducible way. New developments added in this edition include NoSQL databases, the Anaconda Python distribution, graphical libraries like Bokeh, and the use of Github for collaborative development. 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. Answer key. We don't just give you a deal—we give you an edge. Download File PDF Human Biology Lab Manual 12th Edition Answers Edition features new exercises and questions that help students learn and use anatomical language and interpret real-world medical images while learning basic human anatomy.
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