Advanced Deep Learning For Engineers And Scientists
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Advanced Deep Learning For Engineers And Scientists
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Author : Kolla Bhanu Prakash
language : en
Publisher: Springer Nature
Release Date : 2021-07-24
Advanced Deep Learning For Engineers And Scientists written by Kolla Bhanu Prakash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-24 with Technology & Engineering categories.
This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.
Machine Learning
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Author : Andreas Lindholm
language : en
Publisher:
Release Date : 2022
Machine Learning written by Andreas Lindholm and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Machine learning categories.
"This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning"--
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
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
Advanced Machine Learning With Python
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Author : John Hearty
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-07-28
Advanced Machine Learning With Python written by John Hearty and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-28 with Computers categories.
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
Data Driven Science And Engineering
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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton 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 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Advanced Deep Learning With Python
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Author : Ivan Vasilev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-12
Advanced Deep Learning With Python written by Ivan Vasilev and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Computers categories.
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
Machine Learning For Engineers
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Author : Ryan G. McClarren
language : en
Publisher: Springer Nature
Release Date : 2021-09-21
Machine Learning For Engineers written by Ryan G. McClarren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-21 with Technology & Engineering categories.
All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.
Deep Learning In Science
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Author : Pierre Baldi
language : en
Publisher:
Release Date : 2021
Deep Learning In Science written by Pierre Baldi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Machine learning categories.
Learning Deep Learning
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Author : Magnus Ekman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2021-07-19
Learning Deep Learning written by Magnus Ekman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Computers categories.
NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Machine Learning Based Modelling In Atomic Layer Deposition Processes
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Author : Oluwatobi Adeleke
language : en
Publisher: CRC Press
Release Date : 2023-12-15
Machine Learning Based Modelling In Atomic Layer Deposition Processes written by Oluwatobi Adeleke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Technology & Engineering categories.
While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.