Tariq arif (18 Ergebnisse)

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. pp. 336.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Introduction to Deep Learning for Engineers | Using Python and Google Cloud Platform | Tariq M. Arif | Taschenbuch | Synthesis Lectures on Mechanical Engineering | xv | Englisch | 2020 | Springer | EAN 9783031796647 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Zustand: Brand New. 192 pages. 9.19x6.13x0.39 inches. In Stock.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Zustand: New.

Zustand: New.

Zustand: New. In.

Zustand: New.

Zustand: New. 2024. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 192 pages. 9.19x6.13x0.59 inches. In Stock.

Buch. Zustand: Neu. Neuware - 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.…

Buch. Zustand: Neu. Neuware - Thorough reference on technologies, designs, and strategies for electric and hybrid electric vehicles, featuring contributions from international experts Designed for readers who need to review different types of electric and hybrid vehicle designs and strategies in a single book, Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling provides a broad overview of the field with additional resources to explore individual topics in greater depth. Abstracts, case studies, references to key data, and relevant numerical simulations are included throughout the text to aid in reader comprehension. This book introduces the global landscape of hybrid and electric vehicles, covering the available technologies from both a mechanical and electrical engineering perspective, presenting mathematical aspects of modeling and analysis, and surveying emerging trends and economic impacts. It also explains all fundamentals, regulations, policies, perceptions, and market competition aspects of intelligent electric vehicles, as well as how smart electric and hybrid vehicles can be utilized to reduce harmful emissions and reliance on fossil fuels over the lifecycle of a vehicle. Edited by a team of highly qualified academics, with contributions by an array of international experts, Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling includes information on: - Electric machine and inverter designs, maximum speed considerations, component cooling, power density, and material performance- Battery systems, fuel cells, plug-in vehicles, mechanical drives and storage systems, and the role of power electronics tools- The impact of trends and technologies like AI, machine vision, and digital twins, as well as related cyber security considerations- Optimization of manufacturing waste, charging stations, sensing control, road trajectory prediction, and navigation systems- Electrical interfaces to protect against electric shock and cost effectiveness compared to gasoline-powered vehicles Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling is an essential reference on the subject for mechanical engineers, industrial engineers, and academic researchers working in the automotive sector. It is also an ideal learning resource for post-graduate students in the automotive field.…