Introduction To Deep Learning For Engineers
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Introduction To Deep Learning For Engineers
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Author : Tariq M. Arif
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-07-22
Introduction To Deep Learning For Engineers written by Tariq M. Arif and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-22 with Technology & Engineering categories.
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.
Deep Learning For Engineers
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Author : Tariq M. Arif
language : en
Publisher: CRC Press
Release Date : 2024-02-28
Deep Learning For Engineers written by Tariq M. Arif and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-28 with Computers categories.
Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed. This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.
A Brief Introduction To Machine Learning For Engineers
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Author : Osvaldo Simeone
language : en
Publisher:
Release Date : 2018
A Brief Introduction To Machine Learning For Engineers written by Osvaldo Simeone and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with TECHNOLOGY & ENGINEERING categories.
There is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. This presents the problem of where the engineer should start. The answer is often "for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this text is useful" and so on. This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study.A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.
Machine Learning For Engineers
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Author : Osvaldo Simeone
language : en
Publisher: Cambridge University Press
Release Date : 2022-11-03
Machine Learning For Engineers written by Osvaldo Simeone and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-03 with Technology & Engineering categories.
This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.
Deep Learning In Computational Mechanics
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Author : Stefan Kollmannsberger
language : en
Publisher: Springer Nature
Release Date : 2021-08-05
Deep Learning In Computational Mechanics written by Stefan Kollmannsberger and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-05 with Technology & Engineering categories.
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning’s fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book’s main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature’s evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python.
Deep Learning Fundamentals
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Author : Chao Pan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-06-15
Deep Learning Fundamentals written by Chao Pan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-15 with categories.
This book is the first part of the book deep learning with Python write by the same author. If you already purchased deep learning with Python by Chao Pan no need for this book. Are you thinking of learning deep Learning fundamentals, concepts and algorithms? (For Beginners) If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Instead of tough math formulas, this book contains several graphs and images. Book Objectives Have an appreciation for deep learning and an understanding of their fundamental principles. Have an elementary grasp of deep learning concepts and algorithms. Have achieved a technical background in deep learning and neural networks. Target Users The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction Teaching Approach What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Machine Learning Fundamentals Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash deep learning from scratch, this book is for you. No programming experience is required. The present only the fundamentals concepts and algorithms of deep learning. It ll be a good introduction for beginners.Q: Can I loan this book to friends?A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.Q: Does this book include everything I need to become a Machine Learning expert?A: Unfortunately, no. This book is designed for readers taking their first steps in Deep Learning and further learning will be required beyond this book to master all aspects.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].
An Introduction To Deep Learning
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Author : Vibhor Kumar Vishnoi
language : en
Publisher: Xoffencerpublication
Release Date : 2024-03-28
An Introduction To Deep Learning written by Vibhor Kumar Vishnoi and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-28 with Comics & Graphic Novels categories.
In deep learning, an artificial neural network (ANN) stores and processes large amounts of data. This is because artificial neural networks are used in deep learning. It is able to find both overt and covert connections across datasets. When working with deep learning, direct programming is not always necessary. Recent years have seen a meteoric rise in its popularity as a result of developments in processing power and the availability of massive datasets. This is one of the reasons why. For the reason that it was created using artificial designed to learn from large datasets. Deep Learning is a subfield of Machine Learning that use neural networks for modeling and problem solving; its development was spurred by the need to address complex problems. In order to train these networks to deal with challenging problems, the appropriate models must first be solved. Neural networks, which imitate the brain in structure and operation, process and transform data. These tasks are handled by multilayer neural networks consisting of numerous nodes communicating with one another. Fundamental to the idea which are defined by the existence of several layers of connected nodes. It is from this idea that the term "deep neural network" was coined. Because these networks can spot hierarchical patterns and features in the data, it's possible that they can develop elaborate representations of the data. If deep learning algorithms could independently learn and develop themselves depending on the data they were presented, then human engineers might not be needed to manually construct features. Deep learning has been very effective in several fields. These fields include picture identification, natural language processing, voice recognition, and recommendation systems. When training deep neural networks, it is generally necessary to have access to vast volumes of data and have a fast processing speed. Training deep neural networks, on the other hand, has become a great deal less complicated in recent years because to the proliferation of cloud computing and specialized equipment such as Graphics Processing Units (GPUs)
Deep Learning On Embedded Systems
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Author : Tariq M. Arif
language : en
Publisher: John Wiley & Sons
Release Date : 2025-04-29
Deep Learning On Embedded Systems written by Tariq M. Arif and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Technology & Engineering categories.
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.
Introduction To Machine Learning
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Author : Ethem Alpaydin
language : en
Publisher: MIT Press (MA)
Release Date : 2010
Introduction To Machine Learning written by Ethem Alpaydin and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.
Introduction To Machine Learning
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Author : Dr. S. RANGA SWAMY
language : en
Publisher: Shashwat Publication
Release Date : 2021-04-26
Introduction To Machine Learning written by Dr. S. RANGA SWAMY and has been published by Shashwat Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-26 with Computers categories.
Machine learning was built from an engineering perspective, while machine learning was born out of a computer science approach. In the one side the operations may be looked at as two different areas, but they have grown in tandem over the past years and around the same period. Other than the univariate methodology (the conventional way of doing things), there has been a great rise in non-uniform approaches. , algorithmic and graphical simulations are being used for statistical and quantitative trading in all kinds of markets. Also, the functional applicability of Bayesian approaches has been significantly improved by the development of a variety of estimated inference algorithms such as variational Bayes and expectation propagation. Related to the effect of recent kernels, broader versions have had a huge impact on both algorithms and implementations. This textbook provides a detailed exploration of recent innovations in these fields thus describing the basic elements in these fields and thus offering a concise introduction to these fields. The book is accompanied by a great deal of supplementary content, example problems as well as the full collection of figures included in the book.