Generative AI

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What is Generative AI

Generative Artificial Intelligence (AI) refers to a class of algorithms that enable computers to generate new content, whether it’s images, text, music, or even entire virtual environments, autonomously. Unlike traditional AI models that focus on classification or prediction tasks, generative AI aims to create novel outputs that mimic human creativity.

Purpose of Generative AI

The Generative AI course is designed to equip learners with the knowledge and skills needed to harness the power of generative algorithms effectively. Through a combination of theoretical concepts and practical exercises, participants will delve into the underlying principles of generative models such as Generative Adversarial Networks (GANs), Variational Auto Encoders (VAEs), and Transformer architectures.

How it is useful to people if they take the course

Those who undertake the Generative AI course will be well-positioned to drive innovation and solve complex problems in fields such as:

  • Entertainment and Media: Professionals can use generative AI to produce lifelike characters, generate original music compositions, and create immersive storytelling experiences.
  • Design and Creativity: Artists and designers can leverage generative algorithms to generate unique visual designs, architectural blueprints, and interactive installations.
  • Healthcare and Biotechnology: Researchers can utilize generative models to simulate biological processes, design new drug compounds, and generate synthetic medical imagery for training diagnostic algorithms.
  • Marketing and Advertising: Marketers can employ generative AI to personalize advertisements, create virtual try-on experiences, and generate content tailored to individual consumer preferences.
  • Gaming and Virtual Reality: Game developers can utilize generative AI to create procedurally generated game worlds, generate realistic non-player characters (NPCs), and enhance the immersion of virtual reality experiences.

Module 1 - Introduction to Generative AI

1.1 Overview of Generative AI
1.2 Definition and Applications
1.3 Historical Context
1.4 Current Trends and Use Cases
1.5 Fundamental Concepts
1.6 Generative Models vs. Discriminative Models
1.7 Probabilistic Modelling
1.8 Basics of Bayesian Inference

Module 2 - Generative Models

2.1 Introduction to Generative Models
2.2 Types of Generative Models (VAEs, GANs, Autoregressive Models)
2.3 Strengths and Weaknesses
2.4 Variational Autoencoders (VAEs)
2.5 Architecture and Training
2.6 Latent Space Representation

Module 3 - Generative Adversarial Networks (GANs)

3.1 Understanding GANs
3.2 Generator and Discriminator
3.3 Training GANs
3.4 GAN Variants (DCGAN, WGAN, etc.)
3.5 Applications of GANs
3.6 Image Generation
3.7 Style Transfer
3.8 Anomaly Detection

Module 4 - Autoregressive Models

4.1 Introduction to Autoregressive Models
4.2 Sequential Data Generation
4.3 Pixel RNN and Pixel CNN
4.4 Applications of Autoregressive Models
4.5 Natural Language Generation
4.6 Handwriting Generation

Module 5 - Advanced Topics

5.1 Conditional Generation
5.2 Conditional GANs
5.3 Conditional VAEs
5.4 Transfer Learning in Generative Models
5.5 Pre-training and Fine-tuning
5.6 Domain Adaptation

Module 6 - Ethical Considerations

6.1 Bias and Fairness in Generative Models
6.2 Addressing Bias in Training Data
6.3 Fairness Metrics
6.4 Privacy Concerns
6.5 Data Privacy in Generative AI
6.6 GDPR and Ethical Guidelines

Module 7 - Future Trends and Final Projects

7.1 Current Research and Developments
7.2 Open AI’s Latest Models
7.3 Recent Advancements in Generative AI

Course Price - 7000/-

  • Duration
  • 50 Hours
  • Lectures
  • 18
  • Level
  • Advance
  • Language
  • English
  • Certificate
  • Yes
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