Random Tensors Pytorch
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Deep Learning on Embedded Systems
Comprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software Deep Learning On Embedded Systems is a comprehensive guide to the practical implementation of deep learning for engineering tasks through computers and embedded hardware such as Raspberry Pi and Nvidia Jetson Nano. After an introduction to the field, the book provides fundamental knowledge on deep learning, convolutional and recurrent neural networks, computer vision, and basics of Linux terminal and docker engines. This book shows detailed setup steps of Jetson Nano and Raspberry Pi for utilizing essential frameworks such as PyTorch and OpenCV. GPU configuration and dependency installation procedure for using PyTorch is also discussed allowing newcomers to seamlessly navigate the learning curve. A key challenge of utilizing deep learning on embedded systems is managing limited GPU and memory resources. This book outlines a strategy of training complex models on a desktop computer and transferring them to embedded systems for inference. Also, students and researchers often face difficulties with the varying probabilistic theories and notations found in data science literature. To simplify this, the book mainly focuses on the practical implementation part of deep learning using Python programming, low-cost hardware, and freely available software such as Anaconda and Visual Studio Code.To aid in reader learning, questions and answers are included at the end of most chapters. Written by a highly qualified author, Deep Learning On Embedded Systems includes discussion on: Fundamentals of deep learning, including neurons and layers, activation functions, network architectures, hyperparameter tuning, and convolutional and recurrent neural networks (CNNs & RNNs) PyTorch, OpenCV, and other essential framework setups for deep transfer learning, along with Linux terminal operations, docker engine, docker images, and virtual environments in embedded devices. Training models for image classification and object detection with classification, then converting trained PyTorch models to ONNX format for efficient deployment on Jetson Nano and Raspberry Pi. Deep Learning On Embedded Systems serves as an excellent introduction to the field for undergraduate engineering students seeking to learn deep learning implementations for their senior capstone or class projects and graduate researchers and educators who wish to implement deep learning in their research.
Inside Deep Learning
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. About the Technology Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. About the Book Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! What's Inside Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology About the Reader For Python programmers with basic machine learning skills. About the Author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. Quotes Pick up this book, and you won't be able to put it down. A rich, engaging knowledge base of deep learning math, algorithms, and models--just like the title says! - From the Foreword by Kirk Borne Ph.D., Chief Science Officer, DataPrime.ai The clearest and easiest book for learning deep learning principles and techniques I have ever read. The graphical representations for the algorithms are an eye-opening revelation. - Richard Vaughan, Purple Monkey Collective A great read for anyone interested in understanding the details of deep learning. - Vishwesh Ravi Shrimali, MBRDI.
Introduction to Deep Learning for Engineers
This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model. In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case. The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.