Mac eBooks
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 ...
Artificial Intelligence By Example, 2nd Edition
Artificial intelligence (AI) has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervise ...
Artificial intelligence (AI) has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervise ...
Learn TensorFlow 2.0
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. Review the new features of TensorFlow 2.0; Use TensorFlow 2.0 to build machine learning and deep learning models; Perform sequence prediction ...
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. Review the new features of TensorFlow 2.0; Use TensorFlow 2.0 to build machine learning and deep learning models; Perform sequence prediction ...
Apple Device Management
Working effectively with Apple platforms at a corporate or business level includes not only infrastructure, but a mode of thinking that administrators have to adopt to find success. A mode of thinking that forces you to leave 30 years of IT dogma at the door. This book is a guide through how to integrate Apple products in your environment with a minimum of friction. Because the Apple ecosystem is not going away. You'll start by understanding where Apple, third-party software vendors, and the IT community is taking us. What is Mobile Device Management and how does it work under the hood. By understanding how MDM works, you will understand what needs to happen on your networks in order to allow for MDM, as well as the best way to give the least amount of access to the servers or services that's necessary. You'll then look at management agents that do not include MDM, as well as when you will need to use an agent as opposed to when to use other options. Once you can install a managemen ...
Working effectively with Apple platforms at a corporate or business level includes not only infrastructure, but a mode of thinking that administrators have to adopt to find success. A mode of thinking that forces you to leave 30 years of IT dogma at the door. This book is a guide through how to integrate Apple products in your environment with a minimum of friction. Because the Apple ecosystem is not going away. You'll start by understanding where Apple, third-party software vendors, and the IT community is taking us. What is Mobile Device Management and how does it work under the hood. By understanding how MDM works, you will understand what needs to happen on your networks in order to allow for MDM, as well as the best way to give the least amount of access to the servers or services that's necessary. You'll then look at management agents that do not include MDM, as well as when you will need to use an agent as opposed to when to use other options. Once you can install a managemen ...
The Big Book of Machine Learning Use Cases
The world of machine learning is evolving so quickly that it's challenging to find real-life use cases that are relevant to your day-to-day work. That's why we've created this comprehensive guide you can start using right away. Get everything you need - use cases, code samples and notebooks - so you can start putting the Databricks Lakehouse Platform to work today. Plus, you'll get case studies from leading companies like Comcast, Regeneron and Nationwide. Learn how to: - Use dynamic time warping and MLflow to detect sales trends series; - Perform multivariate time series forecasting with recurrent neural networks; - Access new product capabilities with demos; - Detect financial fraud at scale with decision trees and MLflow on Databricks. ...
The world of machine learning is evolving so quickly that it's challenging to find real-life use cases that are relevant to your day-to-day work. That's why we've created this comprehensive guide you can start using right away. Get everything you need - use cases, code samples and notebooks - so you can start putting the Databricks Lakehouse Platform to work today. Plus, you'll get case studies from leading companies like Comcast, Regeneron and Nationwide. Learn how to: - Use dynamic time warping and MLflow to detect sales trends series; - Perform multivariate time series forecasting with recurrent neural networks; - Access new product capabilities with demos; - Detect financial fraud at scale with decision trees and MLflow on Databricks. ...
Automated Machine Learning
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for pract ...
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for pract ...
Python Machine Learning Projects
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don't have one already, then provide you with a conceptual understanding of machine learning in the chapter "An Introduction to Machine Learning." What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. ...
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don't have one already, then provide you with a conceptual understanding of machine learning in the chapter "An Introduction to Machine Learning." What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. ...
AI Crash Course
Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. ...
Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. ...
Mastering Go, 2nd Edition
Often referred to (incorrectly) as Golang, Go is the high-performance systems language of the future. Mastering Go, Second Edition helps you become a productive expert Go programmer, building and improving on the groundbreaking first edition. Mastering Go, Second Edition shows how to put Go to work on real production systems. For programmers who already know the Go language basics, this book provides examples, patterns, and clear explanations to help you deeply understand Go's capabilities and apply them in your programming work. The book covers the nuances of Go, with in-depth guides on types and structures, packages, concurrency, network programming, compiler design, optimization, and more. Each chapter ends with exercises and resources to fully embed your new knowledge. This second edition includes a completely new chapter on machine learning in Go, guiding you from the foundation statistics techniques through simple regression and clustering to classification, neural netwo ...
Often referred to (incorrectly) as Golang, Go is the high-performance systems language of the future. Mastering Go, Second Edition helps you become a productive expert Go programmer, building and improving on the groundbreaking first edition. Mastering Go, Second Edition shows how to put Go to work on real production systems. For programmers who already know the Go language basics, this book provides examples, patterns, and clear explanations to help you deeply understand Go's capabilities and apply them in your programming work. The book covers the nuances of Go, with in-depth guides on types and structures, packages, concurrency, network programming, compiler design, optimization, and more. Each chapter ends with exercises and resources to fully embed your new knowledge. This second edition includes a completely new chapter on machine learning in Go, guiding you from the foundation statistics techniques through simple regression and clustering to classification, neural netwo ...
macOS Catalina: The Missing Manual
Apple gives macOS new features and improvements right on your desktop and under the hood with Catalina - aka OS X 10.15. With this updated guide, you'll learn how to use your iPad as a second screen, work with iPad apps on your Mac and use Screen Time on your Mac. This new edition of the #1 bestselling Mac book shows you how to use the revamped apps for Music, Podcasts, and TV. Loaded with illustrations, step-by-step instructions, tips, and tricks, this book from David Pogue - Missing Manual series creator, New York Times columnist, and Emmy-winning tech correspondent for CNBC, CBS, and NPR - covers everything Catalina has to offer with lots of humor and technical insight. ...
Apple gives macOS new features and improvements right on your desktop and under the hood with Catalina - aka OS X 10.15. With this updated guide, you'll learn how to use your iPad as a second screen, work with iPad apps on your Mac and use Screen Time on your Mac. This new edition of the #1 bestselling Mac book shows you how to use the revamped apps for Music, Podcasts, and TV. Loaded with illustrations, step-by-step instructions, tips, and tricks, this book from David Pogue - Missing Manual series creator, New York Times columnist, and Emmy-winning tech correspondent for CNBC, CBS, and NPR - covers everything Catalina has to offer with lots of humor and technical insight. ...
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 ...