Autoencoders & VAEs
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Home > Computing and Information Technology > Computer science > Artificial intelligence > Neural networks and fuzzy systems > Autoencoders & VAEs: Hands-On Generative AI Projects(4 The Generative AI Blueprint)
Autoencoders & VAEs: Hands-On Generative AI Projects(4 The Generative AI Blueprint)

Autoencoders & VAEs: Hands-On Generative AI Projects(4 The Generative AI Blueprint)


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About the Book

The ultimate project-based guide to mastering autoencoders, VAEs, and creative generative AI with PyTorch and TensorFlow Ever wondered how AI can compress an image into a handful of numbers-and then recreate it with uncanny precision? Or how a neural network can dream up new data-images, sounds, even faces-out of thin air? That's not magic. That's the power of autoencoders and variational autoencoders (VAEs)-and you're about to learn how to wield it. Autoencoders & VAEs: Hands-On Generative AI Projects is your action-packed guide to understanding and building these core generative AI models. No dry lectures. No endless math detours. Just hands-on projects, clear explanations, and real-world applications that bring theory to life. Whether you're a curious beginner, machine learning enthusiast, or a developer looking to add generative AI to your skillset, this book gives you everything you need to get started-and go deep. What You'll Learn: Build autoencoders and VAEs from scratch using PyTorch and TensorFlow Visualize latent space and generate new data samples Use denoising autoencoders to clean up corrupted inputs Create β-VAE, Conditional VAE (CVAE), Ladder VAE, and VAE-GAN hybrids Explore the creative potential of latent variables in generative tasks Deploy your models using Flask, FastAPI, Streamlit, and Gradio Build a mini generative web app to showcase your models Why Autoencoders & VAEs? Autoencoders are one of the most versatile tools in the deep learning toolbox, used for dimensionality reduction, anomaly detection, data denoising, and more. VAEs take it further, enabling data generation, style transfer, and creative AI applications. This book is your step-by-step guide to mastering both, with side-by-side implementations in two of the most popular AI frameworks-so you become bilingual in TensorFlow and PyTorch. Built for Builders This isn't a book for armchair theorists. This is for the tinkerers, the doers, the "what if I break it?" crowd. Every chapter includes: Clear explanations with code walkthroughs Hands-on projects you can build and expand Real-world use cases and practical tips Humor, analogies, and caffeine-powered energy to keep it fun Part of The Generative AI Blueprint Series This is Volume 4 in the best-selling Generative AI Blueprint series. Ready to go deeper? Start with Generative AI for Beginners or Python for AI if you're just getting started Dive into GANs in Action to explore adversarial models Explore Transformers & GPT for cutting-edge language models Learn to scale with Fine-Tuning & Deploying AI Models Who This Book Is For: AI & ML students wanting real-world projects Developers and data scientists expanding into generative AI Hobbyists and indie hackers building creative apps Anyone curious about how machines can learn to recreate and invent Let's Get Building Fire up your environment, load up your datasets, and get ready to turn latent space into your creative playground. Whether you're cleaning images, generating faces, or designing AI art tools, you'll walk away with the skills and confidence to build your own generative AI projects-end to end. Autoencoders & VAEs isn't just a book. It's a launchpad. Let's make some neural magic happen.


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Product Details
  • ISBN-13: 9798298781138
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 279 mm
  • No of Pages: 136
  • Returnable: N
  • Spine Width: 7 mm
  • Weight: 385 gr
  • ISBN-10: 8298781139
  • Publisher Date: 19 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Series Title: 4 The Generative AI Blueprint
  • Sub Title: Hands-On Generative AI Projects
  • Width: 216 mm


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