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Generative AI with Python and TensorFlow 2

Author: Joseph Babcock

Publisher: Packt Publishing Ltd

Published: 2021-04-30

Total Pages: 489

ISBN-13: 1800208502

Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

Generative AI with Python and TensorFlow 2

Author: Joseph Babcock

Publisher:

Published: 2021-04-30

Total Pages: 488

ISBN-13: 9781800200883

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Packed with intriguing real-world projects as well as theory, Generative AI with Python and TensorFlow 2 enables you to leverage artificial intelligence creatively and generate human-like data in the form of speech, text, images, and music.

Deep Learning with TensorFlow 2 and Keras

Author: Antonio Gulli

Publisher: Packt Publishing Ltd

Published: 2019-12-27

Total Pages: 647

ISBN-13: 1838827722

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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

Generative AI with Python and TensorFlow

Author: Anand Vemula

Publisher: Independently Published

Published: 2024-07-03

Total Pages: 0

ISBN-13:

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Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. Introduction Overview of Generative AI: Understand the basic concepts, history, and significance of generative AI. Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation. Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow. Fundamentals of Machine Learning Supervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning. Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI. Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative Models Understanding Generative Models: Explore the theoretical foundations of generative models. Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases. Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications. Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced Topics Transfer Learning for Generative Models: Explore how transfer learning can be applied to generative models. Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously. Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical Applications Image Generation and Style Transfer: Create stunning images and apply style transfer techniques. Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI. Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On Projects Project 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art. Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction. Project 4: Music Generation with RNNs: Create a music generation model using RNNs.

Hands-On Image Generation with TensorFlow

Author: Soon Yau Cheong

Publisher: Packt Publishing Ltd

Published: 2020-12-24

Total Pages: 306

ISBN-13: 1838821104

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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.

Generative Deep Learning

Author: David Foster

Publisher: "O'Reilly Media, Inc."

Published: 2019-06-28

Total Pages: 360

ISBN-13: 1492041890

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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

Learn Python Generative AI

Author: Zonunfeli Ralte

Publisher: BPB Publications

Published: 2024-02-01

Total Pages: 421

ISBN-13: 9355518978

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Learn to unleash the power of AI creativity KEY FEATURES ● Understand the core concepts related to generative AI. ● Different types of generative models and their applications. ● Learn how to design generative AI neural networks using Python and TensorFlow. DESCRIPTION This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making. The book concludes by synthesizing key learnings, offering insights into the future of generative AI, and making it a comprehensive guide for diverse industries. Readers will find themselves equipped with a profound understanding of generative AI, its current applications, and its boundless potential for future innovations. WHAT YOU WILL LEARN ● Acquire practical skills in designing and implementing various generative AI models. ● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval. ● Navigate challenges in healthcare, retail, and finance using sector specific insights. ● Generate images and text with VAEs, GANs, LLMs, and vector databases. ● Focus on both traditional and cutting edge techniques in generative AI. WHO THIS BOOK IS FOR This book is for current and aspiring emerging AI deep learning professionals, architects, students, and anyone who is starting and learning a rewarding career in generative AI. TABLE OF CONTENTS 1. Introducing Generative AI 2. Designing Generative Adversarial Networks 3. Training and Developing Generative Adversarial Networks 4. Architecting Auto Encoder for Generative AI 5. Building and Training Generative Autoencoders 6. Designing Generative Variation Auto Encoder 7. Building Variational Autoencoders for Generative AI 8. Fundamental of Designing New Age Generative Vision Transformer 9. Implementing Generative Vision Transformer 10. Architectural Refactoring for Generative Modeling 11. Major Technical Roadblocks in Generative AI and Way Forward 12. Overview and Application of Generative AI Models 13. Key Learnings

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

Author: ROWEL. ATIENZA

Publisher:

Published: 2020-02-28

Total Pages: 512

ISBN-13: 9781838821654

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Artificial Intelligence with Python

Author: Alberto Artasanchez

Publisher: Packt Publishing Ltd

Published: 2020-01-31

Total Pages: 619

ISBN-13: 1839216077

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New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

Hands-on Computer Vision with TensorFlow 2

Author: Benjamin Planche

Publisher:

Published: 2019

Total Pages: 372

ISBN-13: 9781788830645

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Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be a part of the exciting advances in computer vision.