Practical Machine Learning for Computer Vision - 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 Machine Learning for Computer Vision write by Valliappa Lakshmanan. This book was released on 2021-07-21. Practical Machine Learning for Computer Vision available in PDF, EPUB and Kindle. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Practical Machine Learning for Computer Vision
Practical Machine Learning for Computer Vision - 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 Machine Learning for Computer Vision write by Valliappa Lakshmanan. This book was released on 2021-07-21. Practical Machine Learning for Computer Vision available in PDF, EPUB and Kindle. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Practical Machine Learning for Computer Vision
Practical Machine Learning for Computer Vision - 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 Machine Learning for Computer Vision write by Valliappa Lakshmanan. This book was released on 2021. Practical Machine Learning for Computer Vision available in PDF, EPUB and Kindle.
Practical Deep Learning for Cloud, Mobile, and Edge
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
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
