Course Outline

Introduction to Generative AI

  • What is Generative AI?
  • History and evolution of Generative AI
  • Key concepts and terminology
  • Overview of applications and potential of Generative AI

Fundamentals of Machine Learning

  • Introduction to machine learning
  • Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning
  • Basic algorithms and models
  • Data preprocessing and feature engineering

Deep Learning Basics

  • Neural networks and deep learning
  • Activation functions, loss functions, and optimizers
  • Overfitting, underfitting, and regularization techniques
  • Introduction to TensorFlow and PyTorch

Generative Models Overview

  • Types of generative models
  • Differences between discriminative and generative models
  • Use cases for generative models

Variational Autoencoders (VAEs)

  • Understanding autoencoders
  • The architecture of VAEs
  • Latent space and its significance
  • Hands-on project: Building a simple VAE

Generative Adversarial Networks (GANs)

  • Introduction to GANs
  • The architecture of GANs: Generator and Discriminator
  • Training GANs and challenges
  • Hands-on project: Creating a basic GAN

Advanced Generative Models

  • Introduction to Transformer models
  • Overview of GPT (Generative Pretrained Transformer) models
  • Applications of GPT in text generation
  • Hands-on project: Text generation with a pre-trained GPT model

Ethics and Implications

  • Ethical considerations in Generative AI
  • Bias and fairness in AI models
  • Future implications and responsible AI

Industry Applications of Generative AI

  • Generative AI in art and creativity
  • Applications in business and marketing
  • Generative AI in science and research

Capstone Project

  • Ideation and proposal of a generative AI project
  • Dataset collection and preprocessing
  • Model selection and training
  • Evaluation and presentation of results

Summary and Next Steps

Requirements

  • An understanding of basic programming concepts in Python
  • Experience with basic mathematical concepts, especially probability and linear algebra

Audience

  • Developers
 14 Hours

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