Deep Reinforcement Learning Hands-On, 2nd Edition
Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve ...
Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve ...
Mastering Machine Learning Algorithms, 2nd Edition
Mastering Machine Learning Algorithms, 2nd Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem - including NumPy and Keras - to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensem ...
Mastering Machine Learning Algorithms, 2nd Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem - including NumPy and Keras - to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensem ...
Programming Phoenix 1.4
Don't accept the compromise between fast and beautiful: you can have it all. Phoenix creator Chris McCord, Elixir creator José Valim, and award-winning author Bruce Tate walk you through building an application that's fast and reliable. At every step, you'll learn from the Phoenix creators not just what to do, but why. Packed with insider insights and completely updated for Phoenix 1.4, this definitive guide will be your constant companion in your journey from Phoenix novice to expert as you build the next generation of web applications. Phoenix is the long-awaited web framework based on Elixir, the highly concurrent language that combines a beautiful syntax with rich metaprogramming. The best way to learn Phoenix is to code, and you'll get to attack some interesting problems. Start working with controllers, views, and templates within the first few pages. Build an in-memory context, and then back it with an Ecto database layer, complete with changesets and constraints that keep re ...
Don't accept the compromise between fast and beautiful: you can have it all. Phoenix creator Chris McCord, Elixir creator José Valim, and award-winning author Bruce Tate walk you through building an application that's fast and reliable. At every step, you'll learn from the Phoenix creators not just what to do, but why. Packed with insider insights and completely updated for Phoenix 1.4, this definitive guide will be your constant companion in your journey from Phoenix novice to expert as you build the next generation of web applications. Phoenix is the long-awaited web framework based on Elixir, the highly concurrent language that combines a beautiful syntax with rich metaprogramming. The best way to learn Phoenix is to code, and you'll get to attack some interesting problems. Start working with controllers, views, and templates within the first few pages. Build an in-memory context, and then back it with an Ecto database layer, complete with changesets and constraints that keep re ...
T-SQL Window Functions, 2nd Edition
Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You'll find a detailed section on optimization, plus an extensive collection of business solutions - including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: Use window functions to improve queries you previously built with predicates; Master essential SQL windowing concepts, and efficiently design window functions; Effectively utilize partitioning, ordering, and framing; Gain practical in-depth insight into window aggregate, ranking, offset, and statist ...
Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You'll find a detailed section on optimization, plus an extensive collection of business solutions - including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: Use window functions to improve queries you previously built with predicates; Master essential SQL windowing concepts, and efficiently design window functions; Effectively utilize partitioning, ordering, and framing; Gain practical in-depth insight into window aggregate, ranking, offset, and statist ...
MOS Study Guide for Microsoft Excel Expert Exam MO-201
Demonstrate your expert-level competency with Microsoft Excel! Designed to help you practice and prepare for Microsoft Office Specialist: Microsoft Excel Expert (Excel and Excel 2019) certification, this official Study Guide delivers: In-depth preparation for each MOS objective; Detailed procedures to help build the skills measured by the exam; Hands-on tasks to practice what you've learned; Ready-made practice files. Sharpen the skills measured by these objectives: Manage Workbook Options and Settings; Manage and Format Data; Create Advanced Formulas and Macros; Manage Advanced Charts and Tables. ...
Demonstrate your expert-level competency with Microsoft Excel! Designed to help you practice and prepare for Microsoft Office Specialist: Microsoft Excel Expert (Excel and Excel 2019) certification, this official Study Guide delivers: In-depth preparation for each MOS objective; Detailed procedures to help build the skills measured by the exam; Hands-on tasks to practice what you've learned; Ready-made practice files. Sharpen the skills measured by these objectives: Manage Workbook Options and Settings; Manage and Format Data; Create Advanced Formulas and Macros; Manage Advanced Charts and Tables. ...
Microsoft Azure Sentinel
Microsoft's cloud-based Azure Sentinel helps you fully leverage advanced AI to automate threat identification and response - without the complexity and scalability challenges of traditional Security Information and Event Management (SIEM) solutions. Now, three of Microsoft's leading experts review all it can do, and guide you step-by-step through planning, deployment, and daily operations. Leveraging in-the-trenches experience supporting early customers, they cover everything from configuration to data ingestion, rule development to incident management... even proactive threat hunting to disrupt attacks before you're exploited. Three of Microsoft's leading security operations experts show how to: Use Azure Sentinel to respond to today's fast-evolving cybersecurity environment, and leverage the benefits of its cloud-native architecture; Review threat intelligence essentials: attacker motivations, potential targets, and tactics, techniques, and procedures; Explore Azure Sentinel compo ...
Microsoft's cloud-based Azure Sentinel helps you fully leverage advanced AI to automate threat identification and response - without the complexity and scalability challenges of traditional Security Information and Event Management (SIEM) solutions. Now, three of Microsoft's leading experts review all it can do, and guide you step-by-step through planning, deployment, and daily operations. Leveraging in-the-trenches experience supporting early customers, they cover everything from configuration to data ingestion, rule development to incident management... even proactive threat hunting to disrupt attacks before you're exploited. Three of Microsoft's leading security operations experts show how to: Use Azure Sentinel to respond to today's fast-evolving cybersecurity environment, and leverage the benefits of its cloud-native architecture; Review threat intelligence essentials: attacker motivations, potential targets, and tactics, techniques, and procedures; Explore Azure Sentinel compo ...
Practical Time Series Analysis
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You'll get the guidance you need to confidently: Find and wrangle time series data; Undertake exploratory time series data analysis; Store temporal data ...
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You'll get the guidance you need to confidently: Find and wrangle time series data; Undertake exploratory time series data analysis; Store temporal data ...
Kubernetes Operators
Operators are a way of packaging, deploying, and managing Kubernetes applications. A Kubernetes application doesn't just run on Kubernetes; it's composed and managed in Kubernetes terms. Operators add application-specific operational knowledge to a Kubernetes cluster, making it easier to automate complex, stateful applications and to augment the platform. Operators can coordinate application upgrades seamlessly, react to failures automatically, and streamline repetitive maintenance like backups. Think of Operators as site reliability engineers in software. They work by extending the Kubernetes control plane and API, helping systems integrators, cluster administrators, and application developers reliably deploy and manage key services and components. Using real-world examples, authors Jason Dobies and Joshua Wood demonstrate how to use Operators today and how to create Operators for your applications with the Operator Framework and SDK. Learn how to establish a Kubernetes cluster ...
Operators are a way of packaging, deploying, and managing Kubernetes applications. A Kubernetes application doesn't just run on Kubernetes; it's composed and managed in Kubernetes terms. Operators add application-specific operational knowledge to a Kubernetes cluster, making it easier to automate complex, stateful applications and to augment the platform. Operators can coordinate application upgrades seamlessly, react to failures automatically, and streamline repetitive maintenance like backups. Think of Operators as site reliability engineers in software. They work by extending the Kubernetes control plane and API, helping systems integrators, cluster administrators, and application developers reliably deploy and manage key services and components. Using real-world examples, authors Jason Dobies and Joshua Wood demonstrate how to use Operators today and how to create Operators for your applications with the Operator Framework and SDK. Learn how to establish a Kubernetes cluster ...
Using Asyncio in Python
If you're among the Python developers put off by asyncio's complexity, it's time to take another look. Asyncio is complicated because it aims to solve problems in concurrent network programming for both framework and end-user developers. The features you need to consider are a small subset of the whole asyncio API, but picking out the right features is the tricky part. That's where this practical book comes in. Veteran Python developer Caleb Hattingh helps you gain a basic understanding of asyncio's building blocks - enough to get started writing simple event-based programs. You'll learn why asyncio offers a safer alternative to preemptive multitasking (threading) and how this API provides a simple way to support thousands of simultaneous socket connections. Get a critical comparison of asyncio and threading for concurrent network programming; Take an asyncio walk-through, including a quickstart guide for hitting the ground looping with event-based programming; Learn the differen ...
If you're among the Python developers put off by asyncio's complexity, it's time to take another look. Asyncio is complicated because it aims to solve problems in concurrent network programming for both framework and end-user developers. The features you need to consider are a small subset of the whole asyncio API, but picking out the right features is the tricky part. That's where this practical book comes in. Veteran Python developer Caleb Hattingh helps you gain a basic understanding of asyncio's building blocks - enough to get started writing simple event-based programs. You'll learn why asyncio offers a safer alternative to preemptive multitasking (threading) and how this API provides a simple way to support thousands of simultaneous socket connections. Get a critical comparison of asyncio and threading for concurrent network programming; Take an asyncio walk-through, including a quickstart guide for hitting the ground looping with event-based programming; Learn the differen ...
TinyML
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size - small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures; Work with Arduino and ultra-low-power microcontrollers; Learn the essentials of ML and how to train your own models; Train models to understand audio, image, and accelerometer data; Explore T ...
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size - small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures; Work with Arduino and ultra-low-power microcontrollers; Learn the essentials of ML and how to train your own models; Train models to understand audio, image, and accelerometer data; Explore T ...