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AI and Machine Learning for Coders
If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving
The Definitive Guide to Firebase
Plan how to build a better app, grow it into a business, and earn money from your hard work using Firebase. In this book, Laurence Moroney, Staff Developer Advocate at Google, takes you through each of the 15 Firebase technologies, showing you how to use them with concrete examples. You’ll see how to build cross-platform apps with the three pillars of the Firebase platform: technologies to help you develop apps with a real-time database, remote configuration, cloud messaging, and more; grow your apps with user sharing, search integration, analytics, and more; and earn from your apps with in-app advertising. After reading The Definitive Guide to Firebase, you'll come away empowered to make ...
AI and Machine Learning for On-Device Development
Chapter 2. Introduction to Computer Vision -- Using Neurons for Vision -- Your First Classifier: Recognizing Clothing Items -- The Data: Fashion MNIST -- A Model Architecture to Parse Fashion MNIST -- Coding the Fashion MNIST Model -- Transfer Learning for Computer Vision -- Summary -- Chapter 3. Introduction to ML Kit -- Building a Face Detection App on Android -- Step 1: Create the App with Android Studio -- Step 2: Add and Configure ML Kit -- Step 3: Define the User Interface -- Step 4: Add the Images as Assets -- Step 5: Load the UI with a Default Picture.
Learning TensorFlow.js
Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch
Beginning Web Development, Silverlight, and ASP.NET AJAX
There has been a huge surge in interest in ‘Web 2.0’ technologies over the last couple of years. Microsoft’s contribution to this area has been the ASP.NET AJAX and Silverlight technologies, coupled to a supporting framework of ancillary tools. This book aims to be a no nonsense introduction to these technologies for the rapidly growing number of people who are realizing that they need Microsoft-based ‘Web 2.0’ skills on their CV. It gives people a grounding in the core concepts of the technologies and shows how they can be used together to produce the results that people need. The author has unparalleled experience of introducing people to these technologies.
Sculpting Data for ML
In the contemporary world of Artificial Intelligence and Machine Learning, data is the new oil. For Machine Learning algorithms to work their magic, it is imperative to lay a firm foundation with relevant data. Sculpting Data for ML introduces the readers to the first act of Machine Learning, Dataset Curation. This book puts forward practical tips to identify valuable information from the extensive amount of crude data available at our fingertips. The step-by-step guide accompanies code examples in Python from the extraction of real-world datasets and illustrates ways to hone the skills of extracting meaningful datasets. In addition, the book also dives deep into how data fits into the Machi...
Foundations of WPF
Windows Presentation Foundations (WPF), formerly code-named Avalon, is part of a suite of new technologies collectively known as ‘The WinFX stack’. The suite, coupled with ancillary technologies such as XAML and LINQ provides a powerful addition to the .NET 2.0 Framework for creating applications for Windows Vista, and WinFX-enabled Windows XP computers. This book explains what WPF is, how it can be used and how it fits into the wider picture of new WinFX technologies. Readers get quickly up to speed with new coding techniques and processes needed for successful WPF coding, and receive a thorough practical grounding in how the technologies can be used.
Grokking Machine Learning
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.
Introducing Microsoft WebMatrix
WebMatrix gives developers a "one-stop-shop" for obtaining and installing a complete Microsoft Web stack. This guide explains how to use WebMatrix and utilize the built-in tools for search engine optimization.
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 TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
