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Generative Deep Learning Updated Edition

15 chapters and 72 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
15
Reading time
~ 16h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

15 chapters - 72 sections

Chapter 1: Introduction to Deep Learning

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1.1 Basics of Neural Networks47m1.2 Overview of Deep Learning45m1.3 Recent Advances in Deep Learning27m1.4 Practical Exercises - Chapter 1: Introduction to Deep Learning8m1.5 Chapter Summary - Chapter 1: Introduction to Deep Learning4m

Chapter 2: Understanding Generative Models

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2.1 Concept and Importance28m2.2 Delve Deeper into Types of Generative Models42m2.3 Recent Developments in Generative Models30m2.4 Practical Exercises - Chapter 2: Understanding Generative Models10m2.5 Chapter 2 Summary - Chapter 2: Understanding Generative Models3m

Quiz: Foundations of Deep Learning

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Questions of Chapter 1: Introduction to Deep Learning3mQuestions of Chapter 2: Understanding Generative Models2mAnswers - Quiz: Foundations of Deep Learning1m

Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)

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3.1 Understanding GANs3m3.2 Architecture of GANs31m3.3 Training GANs29m3.4 Evaluating GANs28m3.5 Variations of GANs32m3.6 Use Cases and Applications of GANs39m3.7 Recent Innovations in GANs22m3.8 Practical Exercises - Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)16m3.9 Chapter Summary - Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)4m

Chapter 4: Project Face Generation with GANs

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4.1 Data Collection and Preprocessing7m4.2 Model Creation8m4.3 Training the GAN8m4.4 Generating New Faces8m4.5 Evaluating the Model8m4.6 Enhancing Face Generation with StyleGAN10m4.7 Chapter Summary - Chapter 4: Project Face Generation with GANs4m

Quiz: Generative Adversarial Networks (GAN)

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Questions - Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)2mQuestions - Chapter 4: Project: Face Generation with GANs2mAnswers - Quiz: Generative Adversarial Networks (GAN)1m

Chapter 5: Exploring Variational Autoencoders (VAEs)

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5.1 Understanding VAEs20m5.2 Architecture of VAEs17m5.3 Training VAEs22m5.4 Evaluating VAEs21m5.5 Variations of VAEs (Beta-VAE, Conditional VAE)11m5.6 Use Cases and Applications of VAEs12m5.7 Recent Advances in VAEs16m5.8 Practical Exercises - Chapter 5: Exploring Variational Autoencoders (VAEs)8m5.9 Chapter Summary - Chapter 5: Exploring Variational Autoencoders (VAEs)4m

Chapter 6: Project: Handwritten Digit Generation with VAEs

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6.1 Data Collection and Preprocessing6m6.2 Model Creation8m6.3 Generating New Handwritten Digits8m6.4 Evaluating the Model10m6.5 Enhancing Digit Generation with Beta-VAE8m

Quiz: Variational Autoencoders (VAEs)

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Questions - Quiz: Variational Autoencoders (VAEs)3mAnswers - Quiz: Variational Autoencoders (VAEs)1m

Chapter 7: Understanding Autoregressive Models

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7.1 PixelRNN and PixelCNN13m7.2 Transformer-based Models (GPT, GPT-3, GPT-4)34m7.3 Use Cases and Applications of Autoregressive Models22m7.4 Practical Exercises - Chapter 7: Understanding Autoregressive Models6m7.5 Chapter Summary - Chapter 7: Understanding Autoregressive Models4m

Chapter 8: Project: Text Generation with Autoregressive Models

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8.1 Data Collection and Preprocessing5m8.2 Model Creation6m8.3 Generating Text with the Fine-Tuned Model6m8.4 Evaluating the Model10m

Quiz: Autoregressive Models

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Questions - Quiz: Autoregressive Models3mAnswers - Quiz: Autoregressive Models1m

Chapter 9: Exploring Diffusion Models

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9.1 Understanding Diffusion Models15m9.2 Architecture of Diffusion Models39m9.3 Training Diffusion Models17m9.4 Evaluating Diffusion Models22m9.5 Practical Exercises - Chapter 9: Exploring Diffusion Models6m9.6 Chapter Summary - Chapter 9: Exploring Diffusion Models4m

Chapter 10: Project: Image Generation with Diffusion Models

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10.1 Data Collection and Preprocessing6m10.2 Model Creation10m10.3 Training the Diffusion Model8m10.4 Generating New Images8m10.5 Evaluating the Model9m

Quiz: Diffusion Models of the book which includes

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Questions - Quiz: Diffusion Models of the book which includes3mAnswers - Quiz: Diffusion Models of the book which includes1m