Generative Adversarial Networks In Practice

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Generative Adversarial Networks in Practice

Mehdi Ghayoumi
Generative Adversarial Networks in Practice Book PDF

Author : Mehdi Ghayoumi
Publisher : CRC Press
Page : 665 pages
File Size : 52,7 Mb
Release : 2023-12-20
Category : Computers
ISBN : 9781003805533

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Generative Adversarial Networks in Practice by Mehdi Ghayoumi Pdf

This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key Features: • Guides you through the complex world of GANs, demystifying their intricacies • Accompanies your learning journey with real-world examples and practical applications • Navigates the theory behind GANs, presenting it in an accessible and comprehensive way • Simplifies the implementation of GANs using popular deep learning platforms • Introduces various GAN architectures, giving readers a broad view of their applications • Nurture your knowledge of AI with our comprehensive yet accessible content • Practice your skills with numerous case studies and coding examples • Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples • Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs • Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject • Takes you through GAN applications across different data types, highlighting their versatility • Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research • Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge • Empowers you with the skills and knowledge needed to confidently use GANs in your projects Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.

Generative Adversarial Networks in Practice

Mehdi Ghayoumi
Generative Adversarial Networks in Practice Book PDF

Author : Mehdi Ghayoumi
Publisher : CRC Press
Page : 671 pages
File Size : 41,9 Mb
Release : 2023-12-20
Category : Computers
ISBN : 9781003805496

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Generative Adversarial Networks in Practice by Mehdi Ghayoumi Pdf

This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key Features: Guides you through the complex world of GANs, demystifying their intricacies Accompanies your learning journey with real-world examples and practical applications Navigates the theory behind GANs, presenting it in an accessible and comprehensive way Simplifies the implementation of GANs using popular deep learning platforms Introduces various GAN architectures, giving readers a broad view of their applications Nurture your knowledge of AI with our comprehensive yet accessible content Practice your skills with numerous case studies and coding examples Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject Takes you through GAN applications across different data types, highlighting their versatility Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge Empowers you with the skills and knowledge needed to confidently use GANs in your projects Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.

Generative Adversarial Networks (GANs)

Russ Elektran
Generative Adversarial Networks GANs Book PDF

Author : Russ Elektran
Publisher : Independently Published
Page : 0 pages
File Size : 45,6 Mb
Release : 2024-04-12
Category : Computers
ISBN : 9798322644088

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Generative Adversarial Networks (GANs) by Russ Elektran Pdf

Unlock the power of Generative Adversarial Networks (GANs) with this comprehensive guidebook, designed to take you from a basic understanding to mastering the art and science behind these transformative neural networks. Whether you're a student, researcher, or professional in computer science and artificial intelligence, this book offers an accessible yet thorough exploration of GANs, covering foundational concepts, mathematical principles, diverse architectures, and ground-breaking applications. "Generative Adversarial Networks (GANs)" demystifies complex ideas through a structured presentation, starting with an introduction to GANs, diving into their mathematical underpinnings, and unfolding their architectural intricacies. Learn the best practices for training GANs, navigating common challenges, and evaluating performance to ensure high-quality outcomes. The book not only explains the various types of GANs and their specific uses but also showcases their incredible potential across different sectors-from creating realistic images to advancing drug discovery and beyond. With a step-by-step guide to building your own GAN model, this book empowers you to put theory into practice. It addresses common pitfalls, offers solutions to typical challenges, and provides insights into advanced topics for those looking to push the limits of what GANs can achieve. Whether you're aiming to understand the basic mechanisms of GANs or explore the frontiers of artificial intelligence research, this book is your go-to resource for all things GANs. Embark on this learning journey to leverage the full capabilities of Generative Adversarial Networks and unlock new possibilities in AI and machine learning.

Generative Adversarial Networks and Deep Learning

Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil
Generative Adversarial Networks and Deep Learning Book PDF

Author : Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil
Publisher : CRC Press
Page : 286 pages
File Size : 45,5 Mb
Release : 2023-04-10
Category : Computers
ISBN : 9781000840568

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Generative Adversarial Networks and Deep Learning by Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil Pdf

This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc. Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum

GANs in Action

Vladimir Bok,Jakub Langr
GANs in Action Book PDF

Author : Vladimir Bok,Jakub Langr
Publisher : Simon and Schuster
Page : 367 pages
File Size : 48,7 Mb
Release : 2019-09-09
Category : Computers
ISBN : 9781638354239

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GANs in Action by Vladimir Bok,Jakub Langr Pdf

Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Hands-On Generative Adversarial Networks with PyTorch 1.x

John Hany,Greg Walters
Hands On Generative Adversarial Networks with PyTorch 1 x Book PDF

Author : John Hany,Greg Walters
Publisher : Unknown
Page : 312 pages
File Size : 46,7 Mb
Release : 2019-12-12
Category : Computers
ISBN : 1789530512

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Hands-On Generative Adversarial Networks with PyTorch 1.x by John Hany,Greg Walters Pdf

Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key Features Implement GAN architectures to generate images, text, audio, 3D models, and more Understand how GANs work and become an active contributor in the open source community Learn how to generate photo-realistic images based on text descriptions Book Description With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models. By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems. What you will learn Implement PyTorch's latest features to ensure efficient model designing Get to grips with the working mechanisms of GAN models Perform style transfer between unpaired image collections with CycleGAN Build and train 3D-GANs to generate a point cloud of 3D objects Create a range of GAN models to perform various image synthesis operations Use SEGAN to suppress noise and improve the quality of speech audio Who this book is for This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You'll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.

Generative Adversarial Networks with Python

Jason Brownlee
Generative Adversarial Networks with Python Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 655 pages
File Size : 55,6 Mb
Release : 2019-07-11
Category : Computers
ISBN : 8210379456XXX

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Generative Adversarial Networks with Python by Jason Brownlee Pdf

Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.

Generative Adversarial Networks Projects

Kailash Ahirwar
Generative Adversarial Networks Projects Book PDF

Author : Kailash Ahirwar
Publisher : Packt Publishing Ltd
Page : 310 pages
File Size : 40,5 Mb
Release : 2019-01-31
Category : Mathematics
ISBN : 9781789134193

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Generative Adversarial Networks Projects by Kailash Ahirwar Pdf

Explore various Generative Adversarial Network architectures using the Python ecosystem Key FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is for If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

Learning Generative Adversarial Networks

Kuntal Ganguly
Learning Generative Adversarial Networks Book PDF

Author : Kuntal Ganguly
Publisher : Unknown
Page : 180 pages
File Size : 55,8 Mb
Release : 2017-10-30
Category : Computers
ISBN : 1788396413

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Learning Generative Adversarial Networks by Kuntal Ganguly Pdf

Build image generation and semi-supervised models using Generative Adversarial NetworksAbout This Book* Understand the buzz surrounding Generative Adversarial Networks and how they work, in the simplest manner possible* Develop generative models for a variety of real-world use-cases and deploy them to production* Contains intuitive examples and real-world cases to put the theoretical concepts explained in this book to practical useWho This Book Is ForData scientists and machine learning practitioners who wish to understand the fundamentals of generative models will find this book useful. Those who wish to implement Generative Adversarial Networks and their variant architectures through real-world examples will also benefit from this book. No prior knowledge of generative models or GANs is expected.What You Will Learn* Understand the basics of deep learning and the difference between discriminative and generative models* Generate images and build semi-supervised models using Generative Adversarial Networks (GANs) with real-world datasets* Tune GAN models by addressing the challenges such as mode collapse, training instability using mini batch, feature matching, and the boundary equilibrium technique.* Use stacking with Deep Learning architectures to run and generate images from text.* Couple multiple Generative models to discover relationships across various domains* Explore the real-world steps to deploy deep models in productionIn DetailGenerative models are gaining a lot of popularity among the data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labeling of the data which makes it an interesting system to use. This book will help you to build and analyze the deep learning models and apply them to real-world problems. This book will help readers develop intelligent and creative application from a wide variety of datasets, mainly focusing on visuals or images.The book begins with the basics of generative models, as you get to know the theory behind Generative Adversarial Networks and its building blocks. This book will show you how you can overcome the problem of text to image synthesis with GANs, using libraries like Tensorflow, Keras and PyTorch. Transfering style from one domain to another becomes a headache when working with huge data sets. The author, using real-world examples, will show how you can overcome this. You will understand and train Generative Adversarial Networks and use them in a production environment and learn tips to use them effectively and accurately.Style and approachA step-by-step guide that will teach you the use of appropriate GAN models for image generation, editing and painting, text-to-image synthesis, image style transfer, and cross-domain discovery with Python libraries such as Tensorflow, Keras, and PyTorch.

Generative Deep Learning

David Foster
Generative Deep Learning Book PDF

Author : David Foster
Publisher : "O'Reilly Media, Inc."
Page : 360 pages
File Size : 40,7 Mb
Release : 2019-06-28
Category : Computers
ISBN : 9781492041894

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Generative Deep Learning by David Foster Pdf

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Generative Adversarial Networks Cookbook

Josh Kalin
Generative Adversarial Networks Cookbook Book PDF

Author : Josh Kalin
Publisher : Packt Publishing Ltd
Page : 261 pages
File Size : 42,5 Mb
Release : 2018-12-31
Category : Computers
ISBN : 9781789139587

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Generative Adversarial Networks Cookbook by Josh Kalin Pdf

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook Description Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away. What you will learnStructure a GAN architecture in pseudocodeUnderstand the common architecture for each of the GAN models you will buildImplement different GAN architectures in TensorFlow and KerasUse different datasets to enable neural network functionality in GAN modelsCombine different GAN models and learn how to fine-tune themProduce a model that can take 2D images and produce 3D modelsDevelop a GAN to do style transfer with Pix2PixWho this book is for This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.

Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)

Ponnusamy, Sivaram,Antari, Jilali,Bhaladhare, Pawan R.,Potgantwar, Amol D.,Kalyanaraman, Swaminathan
Enhancing Security in Public Spaces Through Generative Adversarial Networks GANs Book PDF

Author : Ponnusamy, Sivaram,Antari, Jilali,Bhaladhare, Pawan R.,Potgantwar, Amol D.,Kalyanaraman, Swaminathan
Publisher : IGI Global
Page : 437 pages
File Size : 55,7 Mb
Release : 2024-05-16
Category : Computers
ISBN : 9798369335987

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Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs) by Ponnusamy, Sivaram,Antari, Jilali,Bhaladhare, Pawan R.,Potgantwar, Amol D.,Kalyanaraman, Swaminathan Pdf

As the demand for data security intensifies, the vulnerabilities become glaring, exposing sensitive information to potential threats. In this tumultuous landscape, Generative Adversarial Networks (GANs) emerge as a groundbreaking solution, transcending their initial role as image generators to become indispensable guardians of data security. Within the pages of Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs), readers are guided through the intricate world of GANs, unraveling their unique design and dynamic adversarial training. The book presents GANs not merely as a technical marvel but as a strategic asset for organizations, offering a comprehensive solution to fortify cybersecurity, protect data privacy, and mitigate the risks associated with evolving cyber threats. It navigates the ethical considerations surrounding GANs, emphasizing the delicate balance between technological advancement and responsible use.

Generative Adversarial Learning: Architectures and Applications

Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Vasile Palade,Juergen Schmidhuber
Generative Adversarial Learning Architectures and Applications Book PDF

Author : Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Vasile Palade,Juergen Schmidhuber
Publisher : Springer Nature
Page : 355 pages
File Size : 52,7 Mb
Release : 2022-03-11
Category : Technology & Engineering
ISBN : 9783030913908

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Generative Adversarial Learning: Architectures and Applications by Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Vasile Palade,Juergen Schmidhuber Pdf

This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.

Hands-On Generative Adversarial Networks with Keras

Rafael Valle
Hands On Generative Adversarial Networks with Keras Book PDF

Author : Rafael Valle
Publisher : Packt Publishing Ltd
Page : 263 pages
File Size : 48,8 Mb
Release : 2019-05-03
Category : Mathematics
ISBN : 9781789535136

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Hands-On Generative Adversarial Networks with Keras by Rafael Valle Pdf

Develop generative models for a variety of real-world use-cases and deploy them to production Key FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook Description Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learnLearn how GANs work and the advantages and challenges of working with themControl the output of GANs with the help of conditional GANs, using embedding and space manipulationApply GANs to computer vision, NLP, and audio processingUnderstand how to implement progressive growing of GANsUse GANs for image synthesis and speech enhancementExplore the future of GANs in visual and sonic artsImplement pix2pixHD to turn semantic label maps into photorealistic imagesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.

Deep Learning in Practice

Mehdi Ghayoumi
Deep Learning in Practice Book PDF

Author : Mehdi Ghayoumi
Publisher : CRC Press
Page : 143 pages
File Size : 49,8 Mb
Release : 2021-12-01
Category : Business & Economics
ISBN : 9781000483390

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Deep Learning in Practice by Mehdi Ghayoumi Pdf

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.