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Generative Deep Learning with Python
Generative Deep Learning with Python

Generative Deep Learning with Python: Unleashing the Creative Power of AI by Mastering AI and Python

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Profile Icon Cuantum Technologies LLC
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$9.99 $34.99
eBook Jun 2024 276 pages 1st Edition
eBook
$9.99 $34.99
Arrow left icon
Profile Icon Cuantum Technologies LLC
Arrow right icon
$9.99 $34.99
eBook Jun 2024 276 pages 1st Edition
eBook
$9.99 $34.99
eBook
$9.99 $34.99

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Key benefits

  • Comprehensive coverage of deep learning and generative models.
  • In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI.
  • Practical coding exercises & interactive assignments to build your own generative models.

Description

Generative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.

Who is this book for?

This course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.

What you will learn

  • Develop a detailed understanding of deep learning fundamentals
  • Implement and train Generative Adversarial Networks (GANs)
  • Create & utilize Variational Autoencoders for data generation
  • Apply autoregressive models for text generation
  • Explore advanced topics & stay ahead in the field of generative AI
  • Analyze and optimize the performance of generative models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 12, 2024
Length: 276 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207122
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Languages :
Concepts :
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What do you get with eBook?

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Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details

Publication date : Jun 12, 2024
Length: 276 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207122
Category :
Languages :
Concepts :
Tools :

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Table of Contents

17 Chapters
Code Blocks Resource Chevron down icon Chevron up icon
Premium Customer Support Chevron down icon Chevron up icon
Who we are Chevron down icon Chevron up icon
Introduction Chevron down icon Chevron up icon
Chapter 1: Introduction to Deep Learning Chevron down icon Chevron up icon
Chapter 2: Understanding Generative Models Chevron down icon Chevron up icon
Chapter 3: Deep Dive into Generative Adversarial Networks (GANs) Chevron down icon Chevron up icon
Chapter 4: Project: Face Generation with GANs Chevron down icon Chevron up icon
Chapter 5: Exploring Variational Autoencoders (VAEs) Chevron down icon Chevron up icon
Chapter 6: Project: Handwritten Digit Generation with VAEs Chevron down icon Chevron up icon
Chapter 7: Understanding Autoregressive Models Chevron down icon Chevron up icon
Chapter 8: Project: Text Generation with Autoregressive Models Chevron down icon Chevron up icon
Chapter 9: Advanced Topics in Generative Deep Learning Chevron down icon Chevron up icon
Chapter 10: Navigating the Future Landscape of Generative Deep Learning Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
Where to continue? Chevron down icon Chevron up icon
Know more about us Chevron down icon Chevron up icon
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