Python Machine Learning, 3rd Edition
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, ...
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, ...
The Pragmatic Programmer, 20th Anniversary Edition
For twenty years, the lessons from The Pragmatic Programmer have helped a generation of programmers examine the very essence of software development, independent of any particular language, framework, or methodology. This classic title is regularly featured on "Top Ten" lists, and many corporations issue it to their new hires. This new 20th Anniversary Edition offers a fresh look at the modern development landscape, cutting through the "business as usual" and tired advice from the net to help guide you through the next twenty years and beyond. Featuring new tips, new topics, and revisions throughout, you don't want to miss this one. Andy and Dave wrote this influential, classic book to help their clients create better software and rediscover the joy of coding. Almost twenty years later, its advice is still spot on, and the Pragmatic philosophy has spawned hundreds of books, screencasts, and audio books, as well as thousands of careers and success stories. This new edition exam ...
For twenty years, the lessons from The Pragmatic Programmer have helped a generation of programmers examine the very essence of software development, independent of any particular language, framework, or methodology. This classic title is regularly featured on "Top Ten" lists, and many corporations issue it to their new hires. This new 20th Anniversary Edition offers a fresh look at the modern development landscape, cutting through the "business as usual" and tired advice from the net to help guide you through the next twenty years and beyond. Featuring new tips, new topics, and revisions throughout, you don't want to miss this one. Andy and Dave wrote this influential, classic book to help their clients create better software and rediscover the joy of coding. Almost twenty years later, its advice is still spot on, and the Pragmatic philosophy has spawned hundreds of books, screencasts, and audio books, as well as thousands of careers and success stories. This new edition exam ...
Competing with Unicorns
Today's tech unicorns develop software differently. They've developed a way of working that lets them scale like an enterprise while working like a startup. These techniques can be learned. This book takes you behind the scenes and shows you how companies like Google, Facebook, and Spotify do it. Leverage their insights, so your teams can work better together, ship higher-quality product faster, innovate more quickly, and compete with the unicorns. Massively successful tech companies, or Unicorns, have discovered how to take the techniques that made them successful as a startup and scale them to the enterprise level. Amazon, Facebook, Google, and Spotify all work like startups, despite having workforces numbering in the tens of thousands. Ex-Spotify engineer and coach, Jonathan Rasmusson, takes you behind the scenes and shows you how to develop software the way the best companies do it. Learn how to give teams purpose through Missions, empower and trust with Squads, and align lar ...
Today's tech unicorns develop software differently. They've developed a way of working that lets them scale like an enterprise while working like a startup. These techniques can be learned. This book takes you behind the scenes and shows you how companies like Google, Facebook, and Spotify do it. Leverage their insights, so your teams can work better together, ship higher-quality product faster, innovate more quickly, and compete with the unicorns. Massively successful tech companies, or Unicorns, have discovered how to take the techniques that made them successful as a startup and scale them to the enterprise level. Amazon, Facebook, Google, and Spotify all work like startups, despite having workforces numbering in the tens of thousands. Ex-Spotify engineer and coach, Jonathan Rasmusson, takes you behind the scenes and shows you how to develop software the way the best companies do it. Learn how to give teams purpose through Missions, empower and trust with Squads, and align lar ...
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools - data science libraries, frameworks, modules, and toolkits - but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data. Get a crash course in Python; Learn the basics of linear algebra, statistics, and probability - and how and when they're used in data science; Collect, explore, clean, munge, and manipulate data; Dive into the fundamentals of machine ...
To really learn data science, you should not only master the tools - data science libraries, frameworks, modules, and toolkits - but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data. Get a crash course in Python; Learn the basics of linear algebra, statistics, and probability - and how and when they're used in data science; Collect, explore, clean, munge, and manipulate data; Dive into the fundamentals of machine ...
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks - Scikit-Learn and TensorFlow - author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets; Use Scikit-Learn to track an example machine-learning project end-to-end; Explore several training models, including support vector machines, deci ...
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks - Scikit-Learn and TensorFlow - author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets; Use Scikit-Learn to track an example machine-learning project end-to-end; Explore several training models, including support vector machines, deci ...
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 ...
Introducing Python, 2nd Edition
Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you've learned. You'll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. ...
Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you've learned. You'll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. ...
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 ...
Java Performance, 2nd Edition
Coding and testing are generally considered separate areas of expertise. In this practical book, Java expert Scott Oaks takes the approach that anyone who works with Java should be adept at understanding how code behaves in the Java Virtual Machine - including the tunings likely to help performance. This updated second edition helps you gain in-depth knowledge of Java application performance using both the JVM and the Java platform. Developers and performance engineers alike will learn a variety of features, tools, and processes for improving the way the Java 8 and 11 LTS releases perform. While the emphasis is on production-supported releases and features, this book also features previews of exciting new technologies such as ahead-of-time compilation and experimental garbage collections. Understand how various Java platforms and compilers affect performance; Learn how Java garbage collection works; Apply four principles to obtain best results from performance testing; Use the JD ...
Coding and testing are generally considered separate areas of expertise. In this practical book, Java expert Scott Oaks takes the approach that anyone who works with Java should be adept at understanding how code behaves in the Java Virtual Machine - including the tunings likely to help performance. This updated second edition helps you gain in-depth knowledge of Java application performance using both the JVM and the Java platform. Developers and performance engineers alike will learn a variety of features, tools, and processes for improving the way the Java 8 and 11 LTS releases perform. While the emphasis is on production-supported releases and features, this book also features previews of exciting new technologies such as ahead-of-time compilation and experimental garbage collections. Understand how various Java platforms and compilers affect performance; Learn how Java garbage collection works; Apply four principles to obtain best results from performance testing; Use the JD ...
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 ...