Mastering Generative Adversarial Networks From Basics to Advanced Applications


Book Description

"Mastering Generative Adversarial Networks From Basics to Advanced Applications offers a thorough examination of Generative Adversarial Networks (GANs), covering their theoretical foundations, architectural innovations, and practical applications. This book delves into the original GAN model, its evolution, and the mathematical frameworks that support it, while also exploring cutting-edge variants and real-world use cases. Authored by Nagaram Ramesh and V. Bhargavi, it provides a crucial resource for academics, researchers, and practitioners aiming to deepen their understanding and effectively implement GAN technologies.




Mastering AI and Generative AI: From Learning Fundamentals to Advanced Applications


Book Description

This comprehensive guide dives into the fascinating world of Artificial Intelligence (AI) and its cutting-edge subfield, Generative AI. Designed for beginners and enthusiasts alike, it equips you with the knowledge and skills to navigate the complexities of machine learning and unlock the power of AI for advanced applications. Building a Strong Foundation The journey begins with mastering the fundamentals. You'll explore the different approaches to AI, delve into the history of this revolutionary field, and gain a solid understanding of various subfields like Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Delving into Machine Learning Machine learning, the core of AI's learning ability, takes center stage. You'll grasp the difference between supervised and unsupervised learning paradigms, discover popular algorithms like decision trees and neural networks, and learn the importance of data preparation for optimal model performance. Evaluation metrics become your tools to measure how effectively your models are learning. Unveiling the Power of Deep Learning Get ready to explore the intricate world of Deep Learning, a powerful subset of machine learning inspired by the human brain. Demystify neural networks, the building blocks of deep learning, and dive into specialized architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for handling sequential data. Deep learning frameworks become your allies, simplifying the process of building and deploying complex deep learning models. The Art of Machine Creation: Generative AI The book then shifts its focus to the transformative realm of Generative AI. Here, machines not only learn but create entirely new data. Explore different types of generative models, from autoregressive models to variational autoencoders, and witness their applications in text generation, image synthesis, and even music creation. A Deep Dive into Generative Adversarial Networks (GANs) Among generative models, Generative Adversarial Networks (GANs) have captured the imagination of researchers and the public alike. This chapter delves into the intriguing concept of GANs, where a generator model continuously strives to create realistic data while a discriminator model acts as a critic, ensuring the generated data is indistinguishable from real data. You'll explore the training process, the challenges of taming GANs, and best practices for achieving optimal results. Advanced Applications Across Domains The book then showcases the transformative potential of Generative AI across various domains. Witness the power of text generation with RNNs, explore the ethical considerations surrounding deepfakes, and discover how chatbots are revolutionizing communication. In the visual realm, delve into Deep Dream and Neural Style Transfer algorithms, and witness the creation of realistic images and videos with cutting-edge generative models. Mastering AI and Generative AI empowers you to not only understand these revolutionary technologies but also leverage them for advanced applications. As you embark on this journey, be prepared to unlock the boundless potential of machine creation and shape the future of AI.




Generative Deep Learning


Book Description

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




GANs in Action


Book Description

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.




Generative Adversarial Networks for Image-to-Image Translation


Book Description

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. - Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN - Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks - Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis - Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications




Generative Adversarial Networks with Python


Book Description

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




200 Tips for Mastering Generative AI


Book Description

In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.




Machine Learning Mastery: Algorithms and Applications


Book Description

Unlock the power of machine learning with Machine Learning Mastery: Algorithms and Applications. This comprehensive guide covers everything from fundamental concepts to advanced techniques, providing a deep dive into the algorithms that power modern AI and their practical applications across various industries. Whether you're a beginner looking to get started or an experienced practitioner seeking to deepen your knowledge, this book offers a structured and detailed exploration of data preprocessing, supervised and unsupervised learning, reinforcement learning, and deep learning. Learn how to evaluate and optimize models, deploy machine learning solutions, and navigate the ethical and practical challenges of implementing AI in the real world. With case studies and hands-on examples, Machine Learning Mastery is your essential companion on the journey to becoming a proficient machine learning expert.




Mastering PyTorch


Book Description

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.




Mastering Large Language Models


Book Description

Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact