Generative Deep Learning with Python : Unleashing the Creative Power of AI by Mastering AI and Python / Cuantum Technologies LLC.

Format:

Book

Author/Creator:

LLC, Cuantum Technologies, Author.

Cuantum Technologies LLC, author.

Language:

English

Subjects (All):

Deep learning (Machine learning).

Physical Description:

1 online resource (276 pages)

Edition:

First edition.

Place of Publication:

Dallas, TX : Cuantum Technologies LLC, [2023]

Biography/History:

LLC Cuantum Technologies: Cuantum Technologies is a leading innovator in the realm of software development and education, with a special focus on leveraging the power of Artificial Intelligence and cutting-edge technology. They specialize in web-based software development, authoring insightful programming and AI literature, and building captivating web experiences with the intricate use of HTML, CSS, JavaScript, and Three. js. Their diverse array of products includes CuantumAI, a pioneering SaaS offering, and an array of books spanning from Python, NLP, PHP, JavaScript, and beyond. Through their services, they are constantly striving to demystify AI and technology, making it accessible, understandable, and useable for all.

Summary:

Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications.Key FeaturesComprehensive 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.Book DescriptionGenerative 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.What you will learnDevelop a detailed understanding of deep learning fundamentalsImplement and train Generative Adversarial Networks (GANs)Create & utilize Variational Autoencoders for data generationApply autoregressive models for text generationExplore advanced topics & stay ahead in the field of generative AIAnalyze and optimize the performance of generative modelsWho this book is forThis 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.

Contents:

Intro

Code Blocks Resource

Premium Customer Support

Who we are

Our Philosophy:

Our Expertise:

Untitled

Introduction

Chapter 1: Introduction to Deep Learning

1.1 Basics of Neural Networks

1.1.1 What is a Neural Network?

1.1.2 Components of a Neural Network

1.1.3 The Perceptron: Building Block of Neural Networks

1.1.4 Backpropagation and Gradient Descent

1.2 Overview of Deep Learning

1.2.1 What is Deep Learning?

1.2.2 Why Deep Learning?

1.2.3 Deep Learning vs Machine Learning

1.2.4 Types of Deep Learning Models

1.2.5 Challenges and Limitations of Deep Learning

1.3 Practical Exercises

1.3.1 Theoretical Questions

1.3.2 Coding Exercises

Chapter 1 Conclusion

Chapter 2: Understanding Generative Models

2.1 Concept and Importance of Generative Models

2.1.1 What are Generative Models?

2.1.2 Importance of Generative Models

2.1.3 Generative Models vs. Discriminative Models

2.2 Types of Generative Models

2.2.1 Variational Autoencoders (VAEs)

2.2.2 Generative Adversarial Networks (GANs)

2.2.3 Other Types of Generative Models

2.3 Training Generative Models

2.3.1 Loss Functions

2.3.2 Training Procedure

2.3.3 Challenges in Training Generative Models

2.4 Challenges and Solutions in Training Generative Models

2.4.1 Mode Collapse

2.4.2 Vanishing Gradients

2.4.3 Evaluating Generative Models

2.4.4 Code Example

2.5 Practical Exercises

Exercise 2.5.1: Implementing a Variational Autoencoder (VAE)

Exercise 2.5.2: Implementing a Generative Adversarial Network (GAN)

Exercise 2.5.3: Experimenting with Loss Functions

Exercise 2.5.4: Mode Collapse and Potential Solutions

Chapter 2 Conclusion

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

3.1 Understanding GANs

3.1.1 The Generator

3.1.2 The Discriminator.

3.1.3 GAN Training and Objective Function

3.2 Architecture of GANs

3.2.1 Generator

3.2.2 Discriminator

3.2.3 Variations in GAN Architecture

3.3 Training GANs

3.3.1 The Basic Training Process

3.3.2 Common Training Problems and Possible Solutions

3.3.3 Advanced Techniques

3.4 Evaluating GANs

3.4.1 Visual Inspection

3.4.2 Inception Score

3.4.3 Frechet Inception Distance

3.4.4 Precision, Recall, and F1 Score for GANs

3.4.5 Limitations of GAN Evaluation Metrics

3.5 Variations of GANs

3.5.1 Deep Convolutional GANs (DCGANs)

3.5.2 Conditional GANs (CGANs)

3.5.3 Wasserstein GANs (WGANs)

3.5.4 Progressive Growing of GANs (ProGANs)

3.5.5 BigGANs and StyleGANs

3.6 Use Cases and Applications of GANs

3.6.1 Image Synthesis

3.6.3 Data Augmentation

3.6.4 Art and Design

3.6.5 Animation and Gaming

3.7 Practical Exercises

3.7.1 Implementing a Simple GAN

3.7.2 Implementing DCGAN

Chapter 3 Conclusion

Chapter 4: Project: Face Generation with GANs

4.1 Data Collection and Preprocessing

4.1.1 Dataset Splitting

4.2 Model Creation

4.2.2 The Discriminator

4.3 Training the GAN

4.4 Generating New Faces

4.4.1 Generating Images from the GAN

4.4.2 Evaluating the Generated Images

4.4.3 Post-processing and Usage

4.5 Advanced Topics

4.5.1 Understanding Mode Collapse

4.5.2 Advanced Techniques for Evaluating GANs

4.5.3 Tips for Improving Image Quality

4.6 Evaluation and Conclusion

4.6.1 Qualitative Evaluation

4.6.2 Quantitative Evaluation

4.6.3 Reflection and Future Work

4.7 Example of Full Code for the Project

Chapter 4 Conclusion

Chapter 5: Exploring Variational Autoencoders (VAEs)

5.1 Understanding Variational Autoencoders (VAEs)

5.1.1 What is Variational Inference?

5.1.2 Latent Space and Its Significance.

5.2 Architecture of Variational Autoencoders (VAEs)

5.2.1 Encoder Network

5.2.2 Reparameterization Trick

5.2.3 Decoder Network

5.2.4 Variations in VAE Architectures

5.3 Training Variational Autoencoders (VAEs)

5.3.1 Forward Pass

5.3.2 Sampling from Latent Space

5.3.3 Decoding

5.3.4 Loss Calculation

5.3.5 Training Stability

5.3.6 Model Capacity

5.3.7 Choice of Prior

5.4 Evaluating VAEs

5.4.1 Reconstruction Loss

5.4.2 KL Divergence

5.4.3 Sample Quality and Diversity

5.4.4 Latent Space Interpolation

5.4.5 Fréchet Inception Distance (FID) Score

5.5 Variations of VAEs

5.5.1 Conditional Variational Autoencoder (CVAE)

5.5.2 Adversarial Autoencoders (AAEs)

5.5.3 β-VAEs

5.5.4 Implementing a Conditional Variational Autoencoder (CVAE)

5.6 Use Cases and Applications of Variational Autoencoders (VAEs)

5.6.1 Anomaly Detection

5.6.2 Image Generation

5.6.3 Drug Discovery

5.6.4 Music Generation

5.7 Practical Exercises

Chapter 5 Conclusion

Chapter 6: Project: Handwritten Digit Generation with VAEs

6.1 Data Collection and Preprocessing

6.1.1 Dataset Selection

6.1.2 Data Preprocessing

6.2 Model Creation

6.2.1 Encoder

6.2.2 Latent Space Sampling

6.2.3 Decoder

6.2.4 Assembling the VAE

6.3 Training the VAE

6.3.1 Compiling the VAE

6.3.2 Training the VAE

6.4 Generating New Handwritten Digits

6.4.1 Sampling Points from the Latent Space

6.4.2 Decoding Points from the Latent Space

6.5 Evaluating the Model

6.5.2 Quantitative Evaluation

6.6 Example of Full Code for the Project

Chapter 6 Conclusion

Chapter 7: Understanding Autoregressive Models

7.1 PixelRNN and PixelCNN

7.1.1 Understanding PixelRNN

7.1.2 Understanding PixelCNN

7.1.3 Role of Gated Units

7.1.4 Variants of PixelRNN and PixelCNN.

7.1.5 Training PixelRNN and PixelCNN Models

7.2 Transformer-based Models

7.2.1 Vision Transformer (ViT)

7.2.2 Image Transformer

7.2.3 Image GPT

7.3 Use Cases and Applications of Autoregressive Models

7.3.1 Image Generation

7.3.2 Image Completion or Inpainting

7.3.3 Anomaly Detection

7.3.4 Text-to-Image Synthesis

7.4 Advanced Concepts in Autoregressive Models

7.4.1 Current Research Trends

7.4.2 Limitations and Challenges

7.4.3 Future Directions

7.5 Practical Exercises

Exercise 1: Implementation of a Simple Autoregressive Model

Exercise 2: Play with PixelCNN

Exercise 3: Explore Transformer-based Models

Exercise 4: Read and Summarize a Research Paper

Exercise 5: Write a Blog Post

Chapter 7 Conclusion

Chapter 8: Project: Text Generation with Autoregressive Models

8.1 Data Collection and Preprocessing

8.1.1 Dataset Selection

8.1.2 Text Preprocessing

8.2 Model Creation

8.3 Training the Autoregressive Model

8.4 Generating New Text

8.5 Evaluating the Model

8.6 Fine-tuning and Improving the Model

8.6.1 Exploring Different Model Architectures

8.6.2 Adjusting Hyperparameters

8.6.3 Employing Different Techniques for Model Optimization

8.6.4 Exploring Methods for Better Text Generation

8.6.5 Fine-tuning the Model on Specific Domains or Styles

8.7 Complete Code

Chapter 8 Conclusion

Chapter 9: Advanced Topics in Generative Deep Learning

9.1 Improved Training Techniques

9.1.1 Batch Normalization

9.1.2 Spectral Normalization

9.1.3 Gradient Penalty

9.1.4 Instance Normalization

9.1.5 Layer Normalization

9.1.6 Adam Optimizer

9.1.7 Learning Rate Scheduling

9.2 Understanding Mode Collapse

9.2.1 Mitigating Mode Collapse

9.3 Dealing with High Dimensional Data

9.3.1 The Curse of Dimensionality.

9.3.2 Dimensionality Reduction Techniques

9.3.3 Convolutional Neural Networks (CNNs)

9.3.4 Preprocessing and Normalizing High-Dimensional Data

9.4 Incorporating Domain Knowledge into Generative Models

9.4.1 Why Incorporate Domain Knowledge?

9.4.2 Techniques for Incorporating Domain Knowledge

9.5 Future Directions and Emerging Techniques in Generative Deep Learning

9.5.1 Generative Models for 3D and 4D data

9.5.2 Generative Models for Sound and Music

9.5.3 Attention-based Generative Models

9.5.4 Integrating Physical and Domain-Specific Knowledge

9.5.5 Quantum Generative Models

Chapter 9 Conclusion

Chapter 10: Navigating the Future Landscape of Generative Deep Learning

10.1 Emerging Trends in Generative Deep Learning

10.1.1 Increased Model Complexity and Efficiency

10.1.2 Multimodal and Cross-Modal Generative Models

10.1.3 Generative Models for Reinforcement Learning

10.1.4 Generative Models for Data Augmentation

10.2 Impact on Various Industries

10.2.1 Healthcare

10.2.2 Entertainment

10.2.3 Finance

10.3 Ethical Considerations in Generative Deep Learning

10.3.1 Privacy Concerns

10.3.2 Misinformation and Deepfakes

10.3.3 Bias in Generative Models

10.4 Social Implications of Generative Deep Learning

10.4.1 Changes in Content Creation

10.4.2 Job Displacement and New Opportunities

10.4.3 Democratizing Creative Tools

10.4.4 Amplifying Online Interactions

10.5 Policy and Regulatory Outlook

10.5.1 Intellectual Property Rights

10.5.2 Privacy

10.5.3 Deepfakes and Misinformation

10.5.4 Accountability

10.5.5 Regulatory Bodies

10.6 Future Research Directions

10.6.1 Enhanced Quality and Diversity

10.6.2 Interpretable and Controllable Outputs

10.6.3 Fair and Ethical AI

10.6.4 Efficient and Scalable Models.

10.6.5 Multi-modal Generative Models.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: LLC, Cuantum Technologies Generative Deep Learning with Python

ISBN:

9781836207122

OCLC:

1463579936