Automated Deep Learning Using Neural Network Intelligence

Automated Deep Learning Using Neural Network Intelligence Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Automated Deep Learning Using Neural Network Intelligence book. This book definitely worth reading, it is an incredibly well-written.

Automated Deep Learning Using Neural Network Intelligence

Ivan Gridin

Author : Ivan Gridin
Publisher : Unknown
Page : 0 pages
File Size : 55,7 Mb
Release : 2022
Category : Electronic
ISBN : 1484290925

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Automated Deep Learning Using Neural Network Intelligence by Ivan Gridin Pdf

Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn Know the basic concepts of optimization tuners, search space, and trials Apply different hyper-parameter optimization algorithms to develop effective neural networks Construct new deep learning models from scratch Execute the automated Neural Architecture Search to create state-of-the-art deep learning models Compress the model to eliminate unnecessary deep learning layers.

Automated Machine Learning

Frank Hutter,Lars Kotthoff,Joaquin Vanschoren

Author : Frank Hutter,Lars Kotthoff,Joaquin Vanschoren
Publisher : Springer
Page : 223 pages
File Size : 47,6 Mb
Release : 2019-05-17
Category : Computers
ISBN : 9783030053185

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Automated Machine Learning by Frank Hutter,Lars Kotthoff,Joaquin Vanschoren Pdf

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Automated Machine Learning

Adnan Masood

Author : Adnan Masood
Publisher : Packt Publishing
Page : 312 pages
File Size : 47,7 Mb
Release : 2021-02-18
Category : Electronic
ISBN : 1800567685

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Automated Machine Learning by Adnan Masood Pdf

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key Features: Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice Eliminate mundane tasks in data engineering and reduce human errors in machine learning models Find out how you can make machine learning accessible for all users to promote decentralized processes Book Description: Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What You Will Learn: Explore AutoML fundamentals, underlying methods, and techniques Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario Find out the difference between cloud and operations support systems (OSS) Implement AutoML in enterprise cloud to deploy ML models and pipelines Build explainable AutoML pipelines with transparency Understand automated feature engineering and time series forecasting Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems Who this book is for: Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

The Development of Deep Learning Technologies

China Info & Comm Tech Grp Corp

Author : China Info & Comm Tech Grp Corp
Publisher : Springer Nature
Page : 68 pages
File Size : 42,8 Mb
Release : 2020-07-13
Category : Computers
ISBN : 9789811545849

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The Development of Deep Learning Technologies by China Info & Comm Tech Grp Corp Pdf

This book is a part of the Blue Book series “Research on the Development of Electronic Information Engineering Technology in China,” which explores the cutting edge of deep learning studies. A subfield of machine learning, deep learning differs from conventional machine learning methods in its ability to learn multiple levels of representation and abstraction by using several layers of nonlinear modules for feature extraction and transformation. The extensive use and huge success of deep learning in speech, CV, and NLP have led to significant advances toward the full materialization of AI. Focusing on the development of deep learning technologies, this book also discusses global trends, the status of deep learning development in China and the future of deep learning.

Automated Machine Learning and Meta-Learning for Multimedia

Wenwu Zhu,Xin Wang

Author : Wenwu Zhu,Xin Wang
Publisher : Springer Nature
Page : 240 pages
File Size : 51,5 Mb
Release : 2022-01-01
Category : Computers
ISBN : 9783030881320

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Automated Machine Learning and Meta-Learning for Multimedia by Wenwu Zhu,Xin Wang Pdf

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.

Deep Learning for Beginners

Steven Cooper

Author : Steven Cooper
Publisher : Roland Bind
Page : 83 pages
File Size : 49,5 Mb
Release : 2018-11-06
Category : Computers
ISBN : PKEY:6610000123797

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Deep Learning for Beginners by Steven Cooper Pdf

☆★The Best Deep Learning Book for Beginners★☆ If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading. This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies which show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley. This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible. ★★ Grab your copy today and learn ★★ ♦ Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google. ♦ The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems. ♦ The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it. ♦ The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for the integration of various systems via an artificial intelligence system, which is already being used for the home and car functions. ♦ And much more... The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow. This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch. When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in? If you want to get started on deep learning and the concepts that run artificial technologies, don't wait any longer. Scroll up and click the buy now button to get this book today!

Hands-On Deep Learning with Go

Gareth Seneque,Darrell Chua

Author : Gareth Seneque,Darrell Chua
Publisher : Packt Publishing Ltd
Page : 228 pages
File Size : 43,6 Mb
Release : 2019-08-08
Category : Computers
ISBN : 9781789347883

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Hands-On Deep Learning with Go by Gareth Seneque,Darrell Chua Pdf

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key FeaturesGain a practical understanding of deep learning using GolangBuild complex neural network models using Go libraries and GorgoniaTake your deep learning model from design to deployment with this handy guideBook Description Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems. What you will learnExplore the Go ecosystem of libraries and communities for deep learningGet to grips with Neural Networks, their history, and how they workDesign and implement Deep Neural Networks in GoGet a strong foundation of concepts such as Backpropagation and MomentumBuild Variational Autoencoders and Restricted Boltzmann Machines using GoBuild models with CUDA and benchmark CPU and GPU modelsWho this book is for This book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.

Machine Learning Automation with TPOT

Dario Radecic

Author : Dario Radecic
Publisher : Packt Publishing Ltd
Page : 270 pages
File Size : 50,9 Mb
Release : 2021-05-07
Category : Computers
ISBN : 9781800564961

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Machine Learning Automation with TPOT by Dario Radecic Pdf

Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key FeaturesUnderstand parallelism and how to achieve it in Python.Learn how to use neurons, layers, and activation functions and structure an artificial neural network.Tune TPOT models to ensure optimum performance on previously unseen data.Book Description The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level. What you will learnGet to grips with building automated machine learning modelsBuild classification and regression models with impressive accuracy in a short timeDevelop neural network classifiers with AutoML techniquesCompare AutoML models with traditional, manually developed models on the same datasetsCreate robust, production-ready modelsEvaluate automated classification models based on metrics such as accuracy, recall, precision, and f1-scoreGet hands-on with deployment using Flask-RESTful on localhostWho this book is for Data scientists, data analysts, and software developers who are new to machine learning and want to use it in their applications will find this book useful. This book is also for business users looking to automate business tasks with machine learning. Working knowledge of the Python programming language and beginner-level understanding of machine learning are necessary to get started.

Deep Learning Techniques for Automation and Industrial Applications

Pramod Singh Rathore,Sachin Ahuja,Srinivasa Rao Burri,Ajay Khunteta,Anupam Baliyan,Abhishek Kumar

Author : Pramod Singh Rathore,Sachin Ahuja,Srinivasa Rao Burri,Ajay Khunteta,Anupam Baliyan,Abhishek Kumar
Publisher : Wiley-Scrivener
Page : 0 pages
File Size : 41,7 Mb
Release : 2024-07-23
Category : Computers
ISBN : 1394234244

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Deep Learning Techniques for Automation and Industrial Applications by Pramod Singh Rathore,Sachin Ahuja,Srinivasa Rao Burri,Ajay Khunteta,Anupam Baliyan,Abhishek Kumar Pdf

This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. Deep learning algorithms and techniques are found to be useful in various areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. “Deep Learning Techniques for Automation and Industrial Applications” presents a concise introduction to the recent advances in this field of artificial intelligence (AI). The broad-ranging discussion covers the algorithms and applications in AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture, manufacturing, and healthcare. Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts from diverse disciplines from data science to visualization. This book provides state-of-the-art approaches to deep learning covering detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained. Audience The book will be useful to researchers and industry engineers working in information technology, data analytics network security, and manufacturing. Graduate and upper-level undergraduate students in advanced modeling and simulation courses will find this book very useful.

Python Deep Learning

Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca

Author : Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca
Publisher : Packt Publishing Ltd
Page : 379 pages
File Size : 48,5 Mb
Release : 2019-01-16
Category : Computers
ISBN : 9781789349702

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Python Deep Learning by Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca Pdf

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Deep Learning

Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy

Author : Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy
Publisher : Walter de Gruyter GmbH & Co KG
Page : 161 pages
File Size : 45,6 Mb
Release : 2020-06-22
Category : Computers
ISBN : 9783110670905

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Deep Learning by Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy Pdf

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Intelligent Data Engineering and Automated Learning – IDEAL 2019

Hujun Yin,David Camacho,Peter Tino,Antonio J. Tallón-Ballesteros,Ronaldo Menezes,Richard Allmendinger

Author : Hujun Yin,David Camacho,Peter Tino,Antonio J. Tallón-Ballesteros,Ronaldo Menezes,Richard Allmendinger
Publisher : Springer Nature
Page : 376 pages
File Size : 55,8 Mb
Release : 2019-11-07
Category : Computers
ISBN : 9783030336172

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 by Hujun Yin,David Camacho,Peter Tino,Antonio J. Tallón-Ballesteros,Ronaldo Menezes,Richard Allmendinger Pdf

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

Automated Machine Learning

Adnan Masood

Author : Adnan Masood
Publisher : Packt Publishing Ltd
Page : 312 pages
File Size : 44,5 Mb
Release : 2021-02-18
Category : Computers
ISBN : 9781800565524

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Automated Machine Learning by Adnan Masood Pdf

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

Deep Neural Networks and Data for Automated Driving

Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben

Author : Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben
Publisher : Springer Nature
Page : 435 pages
File Size : 51,8 Mb
Release : 2022-07-19
Category : Technology & Engineering
ISBN : 9783031012334

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Deep Neural Networks and Data for Automated Driving by Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Pdf

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh

Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
Publisher : CRC Press
Page : 255 pages
File Size : 55,7 Mb
Release : 2020-10-08
Category : Computers
ISBN : 9781000179538

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Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches by K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh Pdf

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Automated Deep Learning Using Neural Network Intelligence

Automated Deep Learning Using Neural Network Intelligence by Ivan Gridin Pdf

Automated Machine Learning by Frank Hutter,Lars Kotthoff,Joaquin Vanschoren Pdf

Automated Machine Learning by Adnan Masood Pdf

The Development of Deep Learning Technologies by China Info & Comm Tech Grp Corp Pdf

Automated Machine Learning and Meta-Learning for Multimedia by Wenwu Zhu,Xin Wang Pdf

Deep Learning for Beginners by Steven Cooper Pdf

Hands-On Deep Learning with Go by Gareth Seneque,Darrell Chua Pdf

Machine Learning Automation with TPOT by Dario Radecic Pdf

Deep Learning Techniques for Automation and Industrial Applications by Pramod Singh Rathore,Sachin Ahuja,Srinivasa Rao Burri,Ajay Khunteta,Anupam Baliyan,Abhishek Kumar Pdf

Python Deep Learning by Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca Pdf

Deep Learning by Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy Pdf

Intelligent Data Engineering and Automated Learning – IDEAL 2019 by Hujun Yin,David Camacho,Peter Tino,Antonio J. Tallón-Ballesteros,Ronaldo Menezes,Richard Allmendinger Pdf

Automated Machine Learning by Adnan Masood Pdf

Deep Neural Networks and Data for Automated Driving by Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Pdf

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches by K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh Pdf

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