Description
Introduction:
This course provides a practical, hands-on approach to building Generative AI models using two of the most popular deep learning frameworks: TensorFlow and PyTorch. Participants will learn how to implement and train generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), from scratch. The course emphasizes coding and experimentation, offering in-depth tutorials and projects that help learners gain expertise in developing AI models capable of generating new data, images, and content. It is designed for AI practitioners, data scientists, and developers who want to deepen their technical skills in Generative AI.
Prerequisites:
- Intermediate Knowledge of Machine Learning: Participants should have a solid understanding of machine learning concepts and algorithms.
- Experience with Python Programming: Proficiency in Python is required, including familiarity with libraries such as NumPy and pandas.
- Basic Understanding of Deep Learning: Knowledge of neural networks, backpropagation, and deep learning fundamentals is necessary.
- Familiarity with TensorFlow or PyTorch: While prior experience with TensorFlow or PyTorch is recommended, the course will provide a brief introduction to both frameworks.
Table of Contents:
- Introduction to Generative AI and Deep Learning Frameworks
1.1 Overview of Generative AI
1.2 Introduction to TensorFlow and PyTorch
1.3 Setting Up the Development Environment - Building Blocks of Generative Models
2.1 Neural Networks and Autoencoders
2.2 Introduction to GANs and VAEs
2.3 Loss Functions and Optimization Techniques - Implementing GANs with TensorFlow
3.1 Building a Basic GAN Model
3.2 Training and Fine-Tuning GANs
3.3 Applications of GANs: Image Synthesis and Data Augmentation - Implementing VAEs with PyTorch
4.1 Building a VAE from Scratch
4.2 Understanding Latent Space and Variational Inference
4.3 Applications of VAEs: Anomaly Detection and Image Reconstruction - Advanced Topics in Generative AI
5.1 Conditional GANs and VAEs
5.2 Style Transfer and Image-to-Image Translation
5.3 Adversarial Training Techniques - Performance Optimization and Model Evaluation
6.1 Techniques for Scaling and Optimizing Generative Models
6.2 Evaluating Model Performance and Quality
6.3 Debugging and Troubleshooting Common Issues - Hands-On Projects
7.1 Project: Creating High-Resolution Images with GANs
7.2 Project: Implementing a Text-to-Image Generator
7.3 Capstone Project: Building a Custom Generative AI Model for a Real-World Application - Deploying Generative Models
8.1 Model Export and Deployment Strategies
8.2 Integrating Generative Models into Applications
8.3 Case Studies: Real-World Deployments of Generative AI - Future Directions and Emerging Trends
9.1 Exploring the Frontiers of Generative AI Research
9.2 Ethical Considerations and Responsible AI
9.3 The Future of Generative AI in Industry
This course provides a comprehensive understanding of Generative AI and deep learning frameworks, covering both foundational concepts and advanced techniques. By the end, participants will have practical experience building and deploying generative models for real-world applications.
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