Hands-On Q-Learning with Python. Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

Nazia Habib

Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning.By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.

Hands-On Recommendation Systems with Python. Start building powerful and personalized, recommendation engines with Python

Rounak Banik

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible..In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.

Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques

Micheal Lanham

With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python.Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games.By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.

Hands-On Reinforcement Learning with Python. Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Sudharsan Ravichandiran

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.

Hands-On RTOS with Microcontrollers. Building real-time embedded systems using FreeRTOS, STM32 MCUs, and SEGGER debug tools

Brian Amos

A real-time operating system (RTOS) is used to develop systems that respond to events within strict timelines. Real-time embedded systems have applications in various industries, from automotive and aerospace through to laboratory test equipment and consumer electronics. These systems provide consistent and reliable timing and are designed to run without intervention for years.This microcontrollers book starts by introducing you to the concept of RTOS and compares some other alternative methods for achieving real-time performance. Once you've understood the fundamentals, such as tasks, queues, mutexes, and semaphores, you'll learn what to look for when selecting a microcontroller and development environment. By working through examples that use an STM32F7 Nucleo board, the STM32CubeIDE, and SEGGER debug tools, including SEGGER J-Link, Ozone, and SystemView, you'll gain an understanding of preemptive scheduling policies and task communication. The book will then help you develop highly efficient low-level drivers and analyze their real-time performance and CPU utilization. Finally, you'll cover tips for troubleshooting and be able to take your new-found skills to the next level.By the end of this book, you'll have built on your embedded system skills and will be able to create real-time systems using microcontrollers and FreeRTOS.

Hands-On SAS For Data Analysis. A practical guide to performing effective queries, data visualization, and reporting techniques

Harish Gulati

SAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It enables the fast and easy processing of data and helps you gain valuable business insights for effective decision-making. This book will serve as a comprehensive guide that will prepare you for the SAS certification exam.After a quick overview of the SAS architecture and components, the book will take you through the different approaches to importing and reading data from different sources using SAS. You will then cover SAS Base and 4GL, understanding data management and analysis, along with exploring SAS functions for data manipulation and transformation. Next, you'll discover SQL procedures and get up to speed on creating and validating queries. In the concluding chapters, you'll learn all about data visualization, right from creating bar charts and sample geographic maps through to assigning patterns and formats. In addition to this, the book will focus on macro programming and its advanced aspects.By the end of this book, you will be well versed in SAS programming and have the skills you need to easily handle and manage your data-related problems in SAS.

Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes

Giuseppe Ciaburro

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.

Hands-On Time Series Analysis with R. Perform time series analysis and forecasting using R

Rami Krispin

Time-series analysis is the art of extracting meaningful insights from, and revealing patterns in, time-series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series.This book explores the basics of time-series analysis with R and lays the foundation you need to build forecasting models. You will learn how to preprocess raw time-series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data using both descriptive statistics and rich data visualization tools in R including the TSstudio, plotly, and ggplot2 packages. The book then delves into traditional forecasting models such as time-series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also work on advanced time-series regression models with machine learning algorithms such as random forest and Gradient Boosting Machine using the h2o package.By the end of this book, you will have developed the skills necessary for exploring your data, identifying patterns, and building a forecasting model using various traditional and machine learning methods.

Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras

Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.

Hands-On Unsupervised Learning with Python. Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

Giuseppe Bonaccorso

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.

Hands-On Web Scraping with Python. Perform advanced scraping operations using various Python libraries and tools such as Selenium, Regex, and others

Anish Chapagain

Web scraping is an essential technique used in many organizations to gather valuable data from web pages. This book will enable you to delve into web scraping techniques and methodologies.The book will introduce you to the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. You'll use powerful libraries from the Python ecosystem such as Scrapy, lxml, pyquery, and bs4 to carry out web scraping operations. You will then get up to speed with simple to intermediate scraping operations such as identifying information from web pages and using patterns or attributes to retrieve information. This book adopts a practical approach to web scraping concepts and tools, guiding you through a series of use cases and showing you how to use the best tools and techniques to efficiently scrape web pages. You'll even cover the use of other popular web scraping tools, such as Selenium, Regex, and web-based APIs.By the end of this book, you will have learned how to efficiently scrape the web using different techniques with Python and other popular tools.

HashiCorp Terraform Associate (003) Exam Guide. Prepare to pass the Terraform Associate exam on your first attempt

Chandra Mohan Dhanasekaran, Manjunath H. Gowda

This HashiCorp Terraform Associate (003) exam guide simplifies technical content relating to the exam and helps you learn using real-world examples. The book is aligned with the latest exam objectives, which enables you to streamline your learning experience instead of referring to multiple sources for preparation. Moreover, the book is designed to serve as a one-stop solution for readers with varied levels of experience in Terraform.You'll learn how to efficiently provision and manage cloud and on-premises infrastructure using Terraform. As you progress, you’ll focus on essential commands, state management techniques, and best practices. Later chapters will show you how to harness the power of Terraform modules for code reusability and scalability. You’ll also gain insights into advanced topics such as debugging, troubleshooting, and leveraging Terraform Cloud and Terraform Enterprise for collaborative infrastructure management. This book provides you with lifetime access to supplementary practice resources such as mock exams, flashcards, and exam tips from experts.By the end of this book, you’ll have the knowledge and skills you need to confidently tackle the Terraform Associate certification exam and excel in your career.

Haskell Data Analysis Cookbook. Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

Nishant Shukla

HDInsight Essentials. Learn how to build and deploy a modern big data architecture to empower your business

Rajesh Nadipalli

Healthcare Analytics Made Simple. Techniques in healthcare computing using machine learning and Python

Vikas (Vik) Kumar, Shameer Khader

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes.This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed.By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.

Hejtoholik, czyli jak zaszczepić się na hejt, nie wpaść w pułapkę obgadywania oraz nauczyć zarabiać na tych, którzy Cię oczerniają

Michał Wawrzyniak

  Hejt do niedawna stanowił wyłącznie czarną stronę sieci. Przyzwyczailiśmy się do niego, a nawet niesłusznie go zaakceptowaliśmy. Efekt? Hejt wyszedł z monitorów na ulice, a my musimy żyć w świecie, w którym każdy może obrobić nam tyłek i zrobić krzywdę — nie tylko słowem, ale i czynem. Hejter rzuca bezpodstawne, agresywne i krzywdzące opinie na prawo i lewo. Kiedyś cierpiały na tym dzieci w gimnazjach, później prezenterzy telewizyjni. Dziś problem ten niebezpiecznie zbliża się do Ciebie. Gwałtownie topnieją szanse na to, abyś nigdy nie poczuł jego niszczycielskiego wpływu. Co gorsza, zrozumiesz wagę problemu dopiero wtedy, gdy dotknie Cię osobiście. Ten czas nadejdzie, prędzej czy później. Wszyscy doskonale wiemy, że wokół nas żyją ludzie głupi, bezduszni i agresywni. Dlaczego pokornie zgadzamy się na ich obecność w swoim otoczeniu? Nie możemy ich wyeliminować, ale czy naprawdę mamy pozwolić, by zdominowali nas na amen? Może warto otworzyć oczy i coś z tym zrobić? Autor tej książki otwiera ludziom oczy od wielu lat. Co więcej, zaplanował i poprowadził największą w Polsce kampanię crowdfundingową. Ustanowił nowy rekord, który zwrócił na problem hejtu uwagę mediów i otworzył drzwi wielu innym, ważnym społecznie projektom. Pokazał, jak działa jego technika: z minusa na plus. Już wkrótce sam się jej nauczysz. W książce znajdziesz zresztą dużo więcej przydatnych technik, a każda z nich pomoże Ci nie tylko w walce z hejtem, lecz także w osobistym rozwoju.