Deep Learning With Swift For Tensorflow
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Deep Learning with Swift for TensorFlow
About this book Discover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advanced machine learning research. Swift for TensorFlow is a combination of both with support for modern hardware accelerators and more. This book covers the deep learning concepts from fundamentals to advanced research. It also introduces the Swift language for beginners in programming. This book is well suited for newcomers and experts in programming and deep learning alike. After reading this book you should be able to program various state-of-the-art deep learning algorithms yourself. The book covers foundational concepts of machine learning. It also introduces the mathematics required to understand deep learning. Swift language is introduced such that it allows beginners and researchers to understand programming and easily transit to Swift for TensorFlow, respectively. You will understand the nuts and bolts of building and training neural networks, and build advanced algorithms. What You'll Learn: Understand deep learning concepts; Program various deep learning algorithms; Run the algorithms in cloud. Who This Book Is For: Newcomers to programming and/or deep learning, and experienced developers; Experienced deep learning practitioners and researchers who desire to work in user space instead of library space with a same programming language without compromising the speed.
Convolutional Neural Networks with Swift for Tensorflow
Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You’ll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. What You'll Learn Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices Who This Book Is For Developers with Swift programming experience who would like to learn convolutional neural networks by example using Swift for Tensorflow as a starting point.
Deep Learning and its Applications
Author: Dr. S. Manikandan
language: en
Publisher: Quing Publications
Release Date: 2022-12-30
Deep Learning and its Applications book chapter is intended to provide various deep insight about Deep learning in various applications. According to current Industry 4.0 standards, Deep learning on the emerging research area to give various services to IT and ITeS. In this book chapter various real time applications are taken for evaluating deep learning approach. Deep Learning is the subset of machine learning which has further learned results of artificial intelligent applications. Artificial Intelligent is the current scenario for making effective decisions. Here the applications are medical image processing, moving objects, image analysis, classification, clustering, prediction, and restoration used to identify various results. Based on each chapter different problems are taken for evaluation and apply different deep learning principles to find accuracy, precision, and score functions. Supervised and Unsupervised learning techniques, TensorFlow, Yolo classifier and Colabs are used to simulate the applications. In this book chapters are very useful for researchers, students, and faculty community to learn about Deep Learning in current trends.