Make Your First Gan With Pytorch

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Make Your First Gan With Pytorch

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Author : Tariq Rashid
language : en
Publisher: Independently Published
Release Date : 2020-03-14

Make Your First Gan With Pytorch written by Tariq Rashid and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-14 with categories.

A gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch.This beginner-friendly guide will give you hands-on experience: * understanding PyTorch basics * developing your first PyTorch neural network * exploring neural network refinements to improve performance * introduce CUDA GPU accelerationIt will introduce GANs, one of the most exciting areas of machine learning: * introducing the concept step-by-step, in plain English * coding the simplest GAN to develop a good workflow * growing our confidence with an MNIST GAN * progressing to develop a GAN to generate full-colour human faces * experiencing how GANs fail, exploring remedies and improving GAN performance and stabilityBeyond the very basics, readers can explore more sophisticated GANs: * convolutional GANs for generated higher quality images * conditional GANs for generated images of a desired classThe appendices will be useful for students of machine learning as they explain themes often skipped over in many courses: * calculating ideal loss values for balanced GANs * probability distributions and sampling them to create images * carefully chosen examples illustrating how convolutions work * a brief explanation of why gradient descent isn't suited to adversarial machine learning

Hands On Generative Adversarial Networks With Pytorch 1 X

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Author : John Hany
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-12

Hands On Generative Adversarial Networks With Pytorch 1 X written by John Hany and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Computers categories.

Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key FeaturesImplement GAN architectures to generate images, text, audio, 3D models, and moreUnderstand how GANs work and become an active contributor in the open source communityLearn how to generate photo-realistic images based on text descriptionsBook 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 learnImplement PyTorch's latest features to ensure efficient model designingGet to grips with the working mechanisms of GAN modelsPerform style transfer between unpaired image collections with CycleGANBuild and train 3D-GANs to generate a point cloud of 3D objectsCreate a range of GAN models to perform various image synthesis operationsUse SEGAN to suppress noise and improve the quality of speech audioWho 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.

Learn Generative Ai With Pytorch

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Author : Mark Liu
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-28

Learn Generative Ai With Pytorch written by Mark Liu and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Computers categories.

Learn how generative AI works by building your very own models that can write coherent text, create realistic images, and even make lifelike music. Learn Generative AI with PyTorch teaches the underlying mechanics of generative AI by building working AI models from scratch. Throughout, you’ll use the intuitive PyTorch framework that’s instantly familiar to anyone who’s worked with Python data tools. Along the way, you’ll master the fundamentals of General Adversarial Networks (GANs), Transformers, Large Language Models (LLMs), variational autoencoders, diffusion models, LangChain, and more! In Learn Generative AI with PyTorch you’ll build these amazing models: • A simple English-to-French translator • A text-generating model as powerful as GPT-2 • A diffusion model that produces realistic flower images • Music generators using GANs and Transformers • An image style transfer model • A zero-shot know-it-all agent The generative AI projects you create use the same underlying techniques and technologies as full-scale models like GPT-4 and Stable Diffusion. You don’t need to be a machine learning expert—you can get started with just some basic Python programming skills. About the technology Transformers, Generative Adversarial Networks (GANs), diffusion models, LLMs, and other powerful deep learning patterns have radically changed the way we manipulate text, images, and sound. Generative AI may seem like magic at first, but with a little Python, the PyTorch framework, and some practice, you can build interesting and useful models that will train and run on your laptop. This book shows you how. About the book Learn Generative AI with PyTorch introduces the underlying mechanics of generative AI by helping you build your own working AI models. You’ll begin by creating simple images using a GAN, and then progress to writing a language translation transformer line-by-line. As you work through the fun and fascinating projects, you’ll train models to create anime images, write like Hemingway, make music like Mozart, and more. You just need Python and a few machine learning basics to get started. You’ll learn the rest as you go! What's inside • Build an English-to-French translator • Create a text-generation LLM • Train a diffusion model to produce high-resolution images • Music generators using GANs and Transformers About the reader Examples use simple Python. No deep learning experience required. About the author Mark Liu is the founding director of the Master of Science in Finance program at the University of Kentucky. The technical editor on this book was Emmanuel Maggiori.

Gans Mit Pytorch Selbst Programmieren

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Author : Tariq Rashid
language : de
Publisher: O'Reilly
Release Date : 2020-09-15

Gans Mit Pytorch Selbst Programmieren written by Tariq Rashid and has been published by O'Reilly this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-15 with Computers categories.

Neues von Bestsellerautor Tariq Rashid: Eine Einführung in die innovative Deep-Learning-Technik GANs Schritt-für-Schritt-Anleitung zum Erstellen eigener GANs mit PyTorch, regt zum Ausprobieren an GANs (Generative Adversarial Networks) gehören zu den spannendsten neuen Algorithmen im Machine Learning Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und gut nachvollziehbar "Die coolste Idee im Deep Learning in den letzten 20 Jahren" sagt Yann LeCun, einer der weltweit führenden Forscher auf dem Gebiet der neuronalen Netze, über GANs, die Generative Adversarial Networks. Bei dieser noch neuen KI-Technik treten zwei neuronale Netze gegeneinander an mit dem Ziel, Bilder, Ton und Videos zu erzeugen, die vom Original nicht zu unterscheiden sind. Dieses Buch richtet sich an alle, die selbst ausprobieren möchten, wie GANs funktionieren. Tariq Rashid zeigt Ihnen Schritt für Schritt, wie Sie mit dem populären Framework PyTorch Ihre eigenen GANs erstellen und trainieren. Sie starten mit einem sehr einfachen GAN, um einen Workflow einzurichten, und üben erste Techniken anhand der MNIST-Datenbank ein. Mit diesem Wissen programmieren Sie dann ein GAN, das realistische menschliche Gesichter erzeugen kann. Tariq Rashids besondere Fähigkeit, komplexe Ideen verständlich zu erklären, macht das Buch zu einer unterhaltsamen Lektüre.

Hands On Generative Adversarial Networks With Pytorch 1 X

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Author : John Hany
language : en
Publisher:
Release Date : 2019-12-12

Hands On Generative Adversarial Networks With Pytorch 1 X written by John Hany and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Computers categories.

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.

Gan

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Author : 타리크 라시드
language : ko
Publisher: 한빛미디어
Release Date : 2021-03-15

Gan written by 타리크 라시드 and has been published by 한빛미디어 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-15 with Computers categories.

인류에겐 이런 GAN 입문서가 필요했다 『신경망 첫걸음』으로 전 세계 수포자들에게 희망을 준 타리크 라시드가 쓴 파이토치 GAN 입문서. 원리만 알면 머신러닝 분야의 최신 기술 GAN 역시 어렵지 않다. 쉽게 가르쳐주는 사람이 없었던 것뿐이다. 손 글씨부터 풀컬러 연예인 얼굴까지 GAN으로 이미지를 생성해보며 안락하게 GAN을 익힐 수 있게 구성했다. 수학 공식은 줄이고 친절한 그림과 문장으로 개념 원리를 알려준다. 출판사 리뷰 멋진 아이디어는 누구나 쉽게 배울 수 있어야 한다 세계에서 가장 안락한 GAN 입문서 2014년 등장한 GAN은 빠르게 발전하는 머신러닝 분야에서 특히 폭발적인 관심을 모았습니다. 인간이 보기에 그럴듯한 새로운 결과를 기계가 알아서 생성한다는 건 혁신이었습니다. 신경망 분야의 세계적인 석학인 얀 르쿤은 GAN을 “최근 10년 동안 머신러닝 분야에서 나온 제일 멋진 아이디어”라고 평하기도 했습니다. GAN에 대한 연구는 현재도 활발히 진행 중이지만, 신경망과 마찬가지로 원리만 알면 GAN 역시 어렵지 않습니다. 쉽게 가르쳐주는 사람이 없었던 것뿐입니다. 『신경망 첫걸음』으로 전 세계 수포자들에게 희망을 준 타리크 라시드가 이번에는 GAN 입문서를 썼습니다. 멋진 아이디어는 간결하고 쉬운 설명으로 많은 사람에게 알려질 때 더욱 빛난다는 저자의 신념이 책 곳곳에서 고스란히 전해집니다. 『신경망 첫걸음』과 마찬가지로 수학 공식은 줄이고 친절한 그림과 문장으로 개념 원리를 알려줍니다. 기존 신경망에 비해 구현이 까다로울 수 있지만, 파이토치와 구글 코랩을 사용해 구현 시 어려움을 겪지 않게 배려했습니다. 1부에서 파이토치와 신경망 기초를 배우고, 2부에서 MNIST 손 글씨부터 풀컬러 연예인 얼굴까지 GAN으로 생성하며, 3부에서는 합성곱 GAN과 조건부 GAN 등 더 고급 기법을 살펴봅니다. 그림으로 합성곱을 설명하는 챕터 하나만 봐도, 이 책이 세상 어떤 딥러닝 자료보다 친절하다는 사실을 알 수 있습니다. 추천사 제가 읽은 머신러닝, 파이토치, GAN 분야 도서 중에서 최고의 책이었습니다. 설명 순서를 극도로 잘 구성해서 복잡한 아이디어도 이해하기 무척 쉬웠습니다. 세상 모든 교재를 이렇게 쓰면 참 좋겠습니다. - 토머스 롤리 (아마존 독자) GAN을 설명하는 블로그, 논문, 교과서, 동영상은 많지만, 학교 수학만 아는 사람도 이해할 수 있게 설명하는 건 이 책뿐입니다. 저자의 전작 『신경망 첫걸음』을 읽어봤다면 왜 그 책이 아마존 베스트셀러 1위인지 잘 알 겁니다. 저자는 어떤 걸 가르치고 어떤 건 다루지 않을지 숙고하여 가능한 한 많은 사람이 좌절하지 않고 가이드를 따라올 수 있게 했습니다. - 제임스 (굿리즈 독자)

Artificial Intelligence Intelligent Art

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Author : Robin Markus Auer
language : en
Publisher: transcript Verlag
Release Date : 2024-05-06

Artificial Intelligence Intelligent Art written by Robin Markus Auer and has been published by transcript Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-06 with Social Science categories.

As algorithmic data processing increasingly pervades everyday life, it is also making its way into the worlds of art, literature and music. In doing so, it shifts notions of creativity and evokes non-anthropocentric perspectives on artistic practice. This volume brings together contributions from the fields of cultural studies, literary studies, musicology and sound studies as well as media studies, sociology of technology, and beyond, presenting a truly interdisciplinary, state-of-the-art picture of the transformation of creative practice brought about by various forms of AI.

Generative Ai Essentials

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Author : Dr. Priyanka Singh
language : en
Publisher: BPB Publications
Release Date : 2025-01-07

Generative Ai Essentials written by Dr. Priyanka Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-07 with Computers categories.

DESCRIPTION Generative AI is changing the way we think about creativity and problem-solving. This book is your go-to guide for understanding and working with this exciting technology. This book offers a clear introduction to generative AI, starting with basics like machine learning and deep learning. It explains key models, including GANs and VAEs, breaking down their architectures and training methods. You will discover how Transformer models like GPT have transformed natural language processing and enabled advancements in language generation. The book explores practical applications such as image synthesis, style transfer, and text generation, showing how generative AI merges technology with creativity. Advanced topics like reinforcement learning, AI ethics, and bias are also covered. Practical tips for creating your own generative AI models, along with insights into the future of this groundbreaking field, making it an essential resource for AI enthusiasts and professionals. By the end of this book, you will have a firm grasp of generative AI concepts and practical skills to get you started. You will be well-prepared to use cloud platforms like AWS, Azure, and GCP to build and launch powerful generative AI projects. From creating realistic images to crafting natural text, you will explore hands-on examples while tackling important ethical questions. This book gives you the skills and confidence to explore the limitless potential of generative AI. KEY FEATURES ● Learn GANs, VAEs, and Transformers with real-world applications. ● Build scalable generative AI models using AWS, Azure, and GCP. ● Explore ethical AI, creative projects, and future trends in technology. WHAT YOU WILL LEARN ● Build foundational knowledge of generative AI principles and models. ● Apply machine learning and deep learning for creative content generation. ● Leverage GANs, VAEs, and Transformer models in real-world scenarios. ● Master cloud tools for scalable generative AI development. ● Address ethical challenges and implement responsible AI practices. ● Explore advanced applications and future directions of generative AI WHO THIS BOOK IS FOR This book is designed for data scientists, machine learning engineers, software developers, cloud professionals, educators, students, and creative professionals. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative Adversarial Networks 3. Variational Autoencoders 4. Transformer Models and Language Generation 5. Image Generation and Style Transfer 6. Text Generation and Language Models with Real-time Examples 7. Generative AI in Art and Creativity 8. Exploring Advanced Concepts 9. Future Direction and Challenges 10. Building Your Own-Generative AI Models 11. Conclusion and Outlook Appendices

Deep Reinforcement Learning Hands On

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Author : Maxim Lapan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-11-12

Deep Reinforcement Learning Hands On written by Maxim Lapan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-12 with Computers categories.

Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methods Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation Develop deep RL models, improve their stability, and efficiently solve complex environments New content on RL from human feedback (RLHF), MuZero, and transformers Book Description Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion What you will learn Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG Implement RL algorithms using PyTorch and modern RL libraries Build and train deep Q-networks to solve complex tasks in Atari environments Speed up RL models using algorithmic and engineering approaches Leverage advanced techniques like proximal policy optimization (PPO) for more stable training Who this book is for This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it’s also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance

Hands On Image Generation With Tensorflow

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Author : Soon Yau Cheong
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-12-24

Hands On Image Generation With Tensorflow written by Soon Yau Cheong and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-24 with Computers categories.

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch Key FeaturesUnderstand the different architectures for image generation, including autoencoders and GANsBuild models that can edit an image of your face, turn photos into paintings, and generate photorealistic imagesDiscover how you can build deep neural networks with advanced TensorFlow 2.x featuresBook Description The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently. What you will learnTrain on face datasets and use them to explore latent spaces for editing new facesGet to grips with swapping faces with deepfakesPerform style transfer to convert a photo into a paintingBuild and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translationUse iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic imagesBecome well versed in attention generative models such as SAGAN and BigGANGenerate high-resolution photos with Progressive GAN and StyleGANWho this book is for The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You’ll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book.