A Primer on Generative Adversarial Networks
A Primer on Generative Adversarial Networks

Author: Sanaa Kaddoura

Publisher:

Published: 2023

Total Pages: 0

ISBN-13: 9783031326622

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This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics. The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.

A Primer on Generative Adversarial Networks
A Primer on Generative Adversarial Networks

Author: Sanaa Kaddoura

Publisher: Springer Nature

Published: 2023-07-04

Total Pages: 91

ISBN-13: 303132661X

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This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics. The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.

Casey Reas: Making Pictures with Generative Adversarial Networks
Casey Reas: Making Pictures with Generative Adversarial Networks

Author: Casey Reas

Publisher:

Published: 2019-09

Total Pages: 90

ISBN-13: 9781926968476

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In this first non-technical introduction to emerging AI techniques, artist Casey Reas explores what it's like to make pictures with generative adversarial networks (GANs), specifically deep convolutional generative adversarial networks (DCGANs). This text is imagined as a primer for readers interested in creative applications of AI technologies. Ideally, readers will explore the strategies of this emerging field as outlined, and remix them to suit their desires. We hope to inspire future research and collaboration, and to encourage a rigorous discussion about art in the age of machine intelligence.

Primer to Neuromorphic Computing
Primer to Neuromorphic Computing

Author: Harish Garg

Publisher: Elsevier

Published: 2024-11-09

Total Pages: 289

ISBN-13: 0443214824

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Primer to Neuromorphic Computing highlights critical and ongoing research into the diverse applications of neuromorphic computing. It includes an overview of primary scientific concepts for the research topic of neuromorphic computing, such as neurons as computational units, artificial intelligence, machine learning, and neuromorphic models. It also discusses the fundamental design method and organization of neuromorphic architecture. Hardware for neuromorphic systems can be developed by exploiting the magnetic properties of different materials. These systems are more energy efficient and enable faster computation . Magnetic tunnel junctions and magnetic textures can be employed to act as neurons and synapses. Neuromorphic systems have general intelligence like humans as they can apply knowledge gained in one domain to other domains. - Discusses potential neuromorphic applications in computing - Presents current trends and models in neuromorphic computing and neural network hardware architectures - Shows the development of novel devices and hardware to enable neuromorphic computing - Offers information about computation and learning principles for neuromorphic systems - Provides information about Neuromorphic implementations of neurobiological learning algorithms - Discusses biologically inspired neuromorphic systems and devices (including adaptive bio interfacing and hybrid systems consisting of living matter and synthetic matter)

A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples)
A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples)

Author: Murat Durmus

Publisher: Murat Durmus

Published: 2023-02-01

Total Pages: 224

ISBN-13:

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Would you like a quick, profound overview of the most popular machine-learning algorithms? Then this is the book for you.! (This book is also suitable for Beginners) This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Each algorithm is also demonstrated with a simple code example in Python. About the Author Murat Durmus is CEO and founder of AISOMA (a Frankfurt am Main (Germany) based company specializing in AI-based technology development and consulting) and Author of the book "Mindful AI - Reflections on Artificial Intelligence" and "INSIDE ALAN TURING." The following algorithms are covered in this book: • ADABOOST • ADAM OPTIMIZATION • AGGLOMERATIVE CLUSTERING • ARMA/ARIMA MODEL • BERT • CONVOLUTIONAL NEURAL NETWORK • DBSCAN • DECISION TREE • DEEP Q-LEARNING • EFFICIENTNET • FACTOR ANALYSIS OF CORRESPONDENCES • GAN • GMM • GPT-3 • GRADIENT BOOSTING MACHINE • GRADIENT DESCENT • GRAPH NEURAL NETWORKS • HIERARCHICAL CLUSTERING • HIDDEN MARKOV MODEL (HMM) • INDEPENDENT COMPONENT ANALYSIS • ISOLATION FOREST • K-MEANS • K-NEAREST NEIGHBOUR • LINEAR REGRESSION • LOGISTIC REGRESSION • LSTM • MEAN SHIFT • MOBILENET • MONTE CARLO ALGORITHM • MULTIMODAL PARALLEL NETWORK • NAIVE BAYES CLASSIFIERS • PROXIMAL POLICY OPTIMIZATION • PRINCIPAL COMPONENT ANALYSIS • Q-LEARNING • RANDOM FORESTS • RECURRENT NEURAL NETWORK • RESNET • SPATIAL TEMPORAL GRAPH CONVOLUTIONAL NETWORKS • STOCHASTIC GRADIENT DESCENT • SUPPORT VECTOR MACHINE • WAVENET • XGBOOST

GANs in Action
GANs in Action

Author: Vladimir Bok

Publisher: Simon and Schuster

Published: 2019-09-09

Total Pages: 367

ISBN-13: 1638354235

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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.

Angular and Deep Learning Pocket Primer
Angular and Deep Learning Pocket Primer

Author: Oswald Campesato

Publisher: Mercury Learning and Information

Published: 2020-10-13

Total Pages: 360

ISBN-13: 168392472X

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As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic deep learning concepts and incorporate that knowledge into Angular 10 applications. It is intended to be a fast-paced introduction to some basic features of deep learning and an overview of several popular deep learning classifiers. The book includes code samples and numerous figures and covers topics such as Angular 10 functionality, basic deep learning concepts, classification algorithms, TensorFlow, and Keras. Companion files with code and color figures are included. FEATURES: Introduces basic deep learning concepts and Angular 10 applications Covers MLPs (MultiLayer Perceptrons) and CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), GRUs (Gated Recurrent Units), autoencoders, and GANs (Generative Adversarial Networks) Introduces TensorFlow 2 and Keras Includes companion files with source code and 4-color figures. The companion files are also available online by emailing the publisher with proof of purchase at [email protected].

Data Science and Algorithms in Systems
Data Science and Algorithms in Systems

Author: Radek Silhavy

Publisher: Springer Nature

Published: 2023-01-03

Total Pages: 1038

ISBN-13: 3031214382

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This book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured. This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings.

Building Products with Generative AI Kindle Edition
Building Products with Generative AI Kindle Edition

Author: Anand Vemula

Publisher: Anand Vemula

Published:

Total Pages: 61

ISBN-13:

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"Building Products with Generative AI" explores the transformative potential of generative artificial intelligence (AI) in product development. The book delves into various aspects of generative AI, starting with an introduction to Generative Adversarial Networks (GANs) and their applications in product design. It discusses how GANs can generate realistic design variations, explore novel concepts, and enhance creativity and innovation in the design process. Fundamental principles of product design, including design theories, user-centered design methodologies, and design thinking frameworks, are explored to provide a solid foundation for integrating generative AI into the product development pipeline. The book emphasizes the importance of data preparation and training strategies for generative models, highlighting techniques for data collection, curation, preprocessing, and model training. Design generation techniques such as conditional generation, style transfer, and text-to-image synthesis are examined in detail, showcasing how these techniques can be leveraged to generate customized designs, synthesize new design aesthetics, and translate textual descriptions into visual representations. The book also explores how generative AI can be integrated into collaborative design processes, enabling real-time collaboration, feedback loops, and iterative improvement. It addresses ethical and bias concerns in AI-driven design, emphasizing responsible AI development practices to ensure fairness, transparency, and accountability. Through case studies, the book demonstrates real-world applications of generative AI in designing customizable products, providing personalized recommendations, and automating design tasks. It also discusses emerging trends in generative AI, ethical implications, and technical challenges in implementation. In conclusion, "Building Products with Generative AI" offers a comprehensive overview of how generative AI is revolutionizing product development. It provides practical insights, strategies, and techniques for harnessing the power of generative AI to drive creativity, efficiency, and innovation in product design. The book serves as a valuable resource for designers, engineers, and business leaders seeking to leverage generative AI to create groundbreaking products that meet the evolving needs of consumers in the digital age.

Generative AI and LLMs
Generative AI and LLMs

Author: S. Balasubramaniam

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2024-09-23

Total Pages: 290

ISBN-13: 311142507X

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Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.