Overview
The “Advanced Program in Generative AI” builds on the foundational knowledge from the introductory course, delving deeper into generative models like GANs and VAEs. Participants will explore advanced architectures, applications such as multimodal generation and sentiment analysis, and gain hands-on experience with complex coding and implementation
Objectives
At the end of GenAI+: Advanced course, participants will be able to
Prerequisites
- Successful completion of the “Generative AI” course.
- Solid understanding of machine learning concepts and algorithms.
- Proficiency in programming languages such as Python.
- Experience with deep learning frameworks like TensorFlow or PyTorch.
Course Outline
- Overview of Unsupervised Learning
- Clustering Techniques: K-Means and Hierarchical Clustering
- Decision Trees and Random Forests
- Ensemble Learning: Bagging and Boosting
- Recap of Deep Learning Basics
- TensorFlow and Keras: Advanced Syntax
- Optimization Techniques in TensorFlow
- Building and Tuning Artificial Neural Networks (ANN)
- In-depth Understanding of GANs
- Advanced GAN Architectures
- Implementing Wasserstein Loss and Gradient Penalty
- Applications of Generative AI in Creative Industries
- Generating Images from Text using Advanced Architectures
- Multimodal Generation Techniques
- Sentiment Analysis: Concepts and Applications
- Hands-on Sentiment Analysis with Real-world Data
- Introduction to Chatbots and their Types
- Google Dialogflow: Setup and Configuration
- Building Dialogflow Agents with Advanced Features
- Nuance in Chatbot Development: Intents, Entities, and Dialogs
- Recap of Key Learnings
- Recommended References and Further Learning
- Next Steps in Generative AI Mastery
- Final Q&A Session