Subtitled 'A Self-Teaching Introduction', this book is designed for use as a primary introduction to Python and can be used as an introductory text or as a resource for professionals in industry. Author H. Bhasin starts from the language fundamentals, primarily the procedural part of the language, then moves on to introduce the object-oriented paradigms. Other sections of the book deal with data structures and advanced topics like handling multi-dimensional arrays using NumPy and visualization using Matplotlib. Regular expressions and multi-threading have been introduced in the appendices.

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With the subtitle "30+ Programming Projects in Art, Games, and More", this book teaches the fundamentals of computer programming. Author  Sheena Vaidyanathan shows how to code a collection of fun, creative projects using Python. She demonstrates the fundamental ideas of computer programming and the process of computational thinking using illustrations, flowcharts, and pseudocode before apply those essentials to coding exciting projects in Python

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Ethereum is a platform that can be used to run decentralized applications (DApps) and smart contracts that have no central points of failure or control, integrate with a payment network, and operate on an open blockchain. With this practical guide, Andreas M. Antonopoulos and Gavin Wood show how to build smart contracts and DApps on Ethereum and other virtual-machine blockchains.The book is intended to serve both as a reference manual and as a cover-to-cover exploration of Ethereum, starting with a gentle introduction suitable for novice users, and going on through the fundamental tools of Ethereum to technical topics and programming examples.

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This hands-on guide teaches you how to tackle the topics of binary analysis and instrumentation with the aim of becoming proficient in an area typically only mastered by a small group of expert hackers. Dennis Andriesse presents advanced binary analysis topics, such as binary instrumentation, dynamic taint analysis, and symbolic execution, in an accessible way. It will take you from basic concepts to state-of-the-art methods as you dig into topics like code injection, disassembly, dynamic taint analysis, and binary instrumentation.

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Subtitled "Start Writing Code to Wrangle, Analyze, and Visualize Data with R", this book brings together all the foundational skills you need to get started working with data, even if you have no programming or data science experience. Michael Freeman and Joel Ross look at the tools you need to solve professional-level data science problems, including R and the Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered.

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This book shows how to take raw data and transform it for use in computations, tables, graphs, and more. Author Dr Jonathan Carroll shows how to build on simple programming techniques like loops and conditionals to create custom functions. Readers will come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio. If you're comfortable writing formulas in Excel, you're ready for this book

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Designed for beginners with no previous programming experience, this book’s straightforward, step-by-step approach shows you how to use JavaScript to add a wide array of interactive features and effects to your web pages. Author Phil Ballard provides step-by-step instructions to walk you through the most common JavaScript programming tasks.The book has practical, hands-on examples to show you how to apply what you learn, and quizzes and exercises help you test your knowledge and stretch your skills.

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This is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, author Rowel Atienza shows hands-on projects demonstrating how to create more effective AI with the latest techniques. The book includes descriptions of GANs, Variational Autoencoders, and Deep Reinforcement Learning.

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This book teaches the basics of using the micro:bit, an open source hardware ARM-based embedded system used to teach computer programming, to build a series of 10 different gadgets from scratch. Author Eric Hagan shows how simple electrical devices like speakers, motors, buzzers and fans work, and explains electronic components like resistors, force resistors, photoresistors, LEDs, and Op Amps, as well as Infrared distance sensors and soil moisture sensors. The book also takes a beginner's look at micropython, one of the fastest-growing computer languages. Projects in this book combine multiple disciplines -- electronics, programming, and engineering -- to build a series of successful gadgets.

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The premise of this book is that component-based applications make it easier to navigate code bases, implement new features, and keep tests fast. Writing for programmers and software team leads who are comfortable with Ruby and Rails, author Stephan Hagemann introduces a practical, start-to-finish methodology for modernizing and restructuring existing Rails applications. One step at a time, he demonstrates how to revamp Rails applications to exhibit visible, provably independent, and explicitly connected parts – thereby simplifying them and making them far easier for teams to manage, change, and test. Throughout, he introduces design concepts and techniques you can use to improve applications of many kinds, even if they weren’t built with Rails or Ruby.

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This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient and aims to show systems engineers and application developers how these long-established patterns provide a common language and framework for dramatically increasing the quality of systems. Author Brendan Burns - Director of Engineering at Microsoft Azure - demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications.

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This book shows how to get to grips with Hidden Markov Models and different inference algorithms by working on real-world problems. It covers Markov processes, models, and types, the different algorithms used in inferences and how to apply them in state and parameter inference. Use of HMMs in time series analysis and natural language processing (NLP) using Python is also covered, as well as the use of HMM for reinforcement learning with the help of Q-Learning. Ankur Ankan and Abinash Panda use hands-on examples to simplify the process flow in machine learning by using Markov model concepts.

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