Practical Deep Learning for Cloud, Mobile, and Edge - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Deep Learning for Cloud, Mobile, and Edge write by Anirudh Koul. This book was released on 2019-10-14. Practical Deep Learning for Cloud, Mobile, and Edge available in PDF, EPUB and Kindle. 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. Relying on years of 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
Practical Deep Learning for Cloud, Mobile & Edge
Practical Deep Learning for Cloud, Mobile & Edge - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Deep Learning for Cloud, Mobile & Edge write by Anirudh Koul. This book was released on 2020. Practical Deep Learning for Cloud, Mobile & Edge available in PDF, EPUB and Kindle.
Deep Learning for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning for Coders with fastai and PyTorch write by Jeremy Howard. This book was released on 2020-06-29. Deep Learning for Coders with fastai and PyTorch available in PDF, EPUB and Kindle. Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
TinyML
TinyML - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook TinyML write by Pete Warden. This book was released on 2019-12-16. TinyML available in PDF, EPUB and Kindle. 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
Practical Deep Learning for Cloud and Mobile
Practical Deep Learning for Cloud and Mobile - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Deep Learning for Cloud and Mobile write by Siddha Ganju. This book was released on 2019. Practical Deep Learning for Cloud and Mobile available in PDF, EPUB and Kindle. Whether you're a software engineer aspiring to enter the world of artificial intelligence, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical applications using deep neural networks for the cloud and mobile using a hands-on approach. Relying on years of 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 can use in the real world. Train, optimize, and deploy computer vision models with Keras, TensorFlow, CoreML, TensorFlow Lite, and MLKit, rapidly taking your system from zero to production quality. Develop AI applications for the desktop, cloud, smartphones, browser, and smart robots using Raspberry Pi, Jetson Nano, and Google Coral Perform Object Classification, Detection, Segmentation in real-time Learn by building examples such as Silicon Valley's "Not Hotdog" app, image search engines, and Snapchat filters Train an autonomous car in a video game environment and then build a real mini version Use transfer learning to train models in minutes Generate photos from sketches in your browser with Generative Adversarial Networks (GANs with pix2pix), and Body Pose Estimation (PoseNet) Discover 50+ practical tips for data collection, model interoperability, debugging, avoiding bias, and scaling to millions of users.
