Python Reinforcement Learning Projects
Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.
In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.
By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We ho ...
IBM Watson Projects
IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.
The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.
By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience. ...
Keras Reinforcement Learning Projects
Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library.
The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You'll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You'll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes.
Once you've understood the basics, you'll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning ...
CompTIA Project+ Certification Guide
The CompTIA Project+ exam is designed for IT professionals who want to improve their career trajectory by gaining certification in project management specific to their industry. This guide covers everything necessary to pass the current iteration of the Project+ PK0-004 exam.
The CompTIA Project+ Certification Guide starts by covering project initiation best practices, including an understanding of organizational structures, team roles, and responsibilities. You'll then study best practices for developing a project charter and the scope of work to produce deliverables necessary to obtain formal approval of the end result. The ability to monitor your project work and make changes as necessary to bring performance back in line with the plan is the difference between a successful and unsuccessful project. The concluding chapters of the book provide best practices to help keep an eye on your projects and close them out successfully. The guide also includes pr ...
Foundations for Architecting Data Solutions
While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects.
Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project.
Start the planning process by considering the key data project types; Use guidelines to evaluate and select data management solutions; Reduce risk related to technology, your team, and vague requirements; Explore system inter ...
Head First PMP, 4th Edition
Head First PMP teaches you the latest principles and certification objectives in The PMBOK Guide in a unique and inspiring way. This updated fourth edition takes you beyond specific questions and answers with a unique visual format that helps you grasp the big picture of project management. By putting PMP concepts into context, you'll be able to understand, remember, and apply them—not just on the exam, but on the job. No wonder so many people have used Head First PMP as their sole source for passing the PMP exam.
Learn PMP's underlying concepts to help you understand the PMBOK principles and pass the certification exam with flying colors; Get 100% coverage of the latest principles and certification objectives in The PMBOK Guide, Fifth Edition; Make use of a thorough and effective preparation guide with hundreds of practice questions and exam strategies; Explore the material through puzzles, games, problems, and exercises that make learning easy and entertaining. ...
Ethereum Projects for Beginners
Ethereum enables the development of efficient, smart contracts that contain code. These smart contracts can interact with other smart contracts to make decisions, store data, and send Ether to others.Ethereum Projects for Beginners provides you with a clear introduction to creating cryptocurrencies, smart contracts, and decentralized applications. As you make your way through the book, you'll get to grips with detailed step-by-step processes to build advanced Ethereum projects. Each project will teach you enough about Ethereum to be productive right away. You will learn how tokenization works, think in a decentralized way, and build blockchain-based distributed computing systems. Towards the end of the book, you will develop interesting Ethereum projects such as creating wallets and secure data sharing.By the end of this book, you will be able to tackle blockchain challenges by implementing end-to-end projects using the full power of the Ethereum blockchain. ...
Learning Bootstrap 4 by Building Projects
Learning Bootstrap 4 by Building Projects covers the essentials of Bootstrap 4 along with best practices. The book begins by introducing you to the latest features of Bootstrap 4. You will learn different elements and components of Bootstrap, such as the strict grid system, Sass, which replaced Less, flexbox, Font Awesome, and cards. As you make your way through the chapters, you will use a template that will help you to build different kinds of real-world websites, such as a social media website, a company landing page, a media hosting website, and a profile page, with ease.
By the end of this book, you will have built websites that are visually appealing, responsive, and robust. ...
Python Artificial Intelligence Projects for Beginners
Artificial Intelligence (AI) is the newest technology that's being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.
This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.
By the end of this book, you will be ...