The Best Tensorflow Books of All Time

.

Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.

Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps that make up a machine learning pipeline. Build your pipeline using components from TensorFlow Extended. Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow and Kubeflow Pipelines. Work with data using TensorFlow Data Validation and TensorFlow Transform. Analyze a model in detail using TensorFlow Model Analysis. Examine fairness and bias in your model performance. Deploy models with TensorFlow Serving or convert them to TensorFlow Lite for mobile devices. Understand privacy-preserving machine learning techniques.

** Featured as a learning resource on the official Keras website **. Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.

Relying on decades of combined industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies.Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.Use transfer learning to train models in minutes.Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.List of ChaptersExploring the Landscape of Artificial IntelligenceWhat's in the Picture: Image Classification with KerasCats Versus Dogs: Transfer Learning in 30 Lines with KerasBuilding a Reverse Image Search Engine: Understanding EmbeddingsFrom Novice to Master Predictor: Maximizing Convolutional Neural Network AccuracyMaximizing Speed and Performance of TensorFlow: A Handy ChecklistPractical Tools, Tips, and TricksCloud APIs for Computer Vision: Up and Running in 15 MinutesScalable Inference Serving on Cloud with TensorFlow Serving and KubeFlowAI in the Browser with TensorFlow.js and ml5.jsReal-Time Object Classification on iOS with Core MLNot Hotdog on iOS with Core ML and Create MLShazam for Food: Developing Android Apps with TensorFlow Lite and ML KitBuilding the Purrfect Cat Locator App with TensorFlow Object Detection APIBecoming a Maker: Exploring Embedded AI at the EdgeSimulating a Self-Driving Car Using End-to-End Deep Learning with KerasBuilding an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacerGuest-contributed ContentThe book features chapters from the following industry experts:Sunil Mallya (Amazon AWS DeepRacer)Aditya Sharma and Mitchell Spryn (Microsoft Autonomous Driving Cookbook)Sam Sterckval (Edgise)Zaid Alyafeai (TensorFlow.js)The book also features content contributed by several industry veterans including François Chollet (Keras, Google), Jeremy Howard (Fast.ai), Pete Warden (TensorFlow Mobile), Anima Anandkumar (NVIDIA), Chris Anderson (3D Robotics), Shanqing Cai (TensorFlow.js), Daniel Smilkov (TensorFlow.js), Cristobal Valenzuela (ml5.js), Daniel Shiffman (ml5.js), Hart Woolery (CV 2020), Dan Abdinoor (Fritz), Chitoku Yato (NVIDIA Jetson Nano), John Welsh (NVIDIA Jetson Nano), and Danny Atsmon (Cognata).