ATC-GENAIF - Introduction to Generative AI - Foundational

Generative AI involves creating models that can generate new, realistic data, such as images, text, or audio, rather than simply recognizing patterns.

This field, epitomized by Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), has applications ranging from content creation to data augmentation in machine learning.

Generative AI fundamentals encompass understanding the foundational concepts and architectures behind generative models, such as GANs and VAEs.

Mastery of probability, statistics, and neural networks forms the basis for delving into the creation and manipulation of artificial data through generative processes.

Duration: 1.0 day

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The idea of this course is to understand fundamentals of Generative AI and it's underline functionality of improving prompt engineering and working with different LLMs and Generative AI models.


Module 1: Machine Learning Fundamental

  • Training Simple Machine Learning Algorithms for Classification
  • Artificial neurons – a brief glimpse into the early history of machine learning

Module 2: What is Generative AI?

  • Why is Now the Best Time to Embrace Generative AI?
  • Enterprise Use Cases for Generative AI

Best Place to Build with Generative AI?

Module 3: Foundation Models & LLMS

  • Introduction to foundation models.
  • Pre training a new foundation model.
  • Picking the right foundation model

Module 4: Reinforcement learning

  • Introduction – learning from experience.
  • Reinforcement learning algorithms.
  • Implementing our first RL algorithm
  • A glance at deep Q-learning

Module 5: Generative Adversial Networks (GANs)

  • Generator, Discriminator, Applications of GAN

Module 6: Finding Success with Generative AI

  • Assessing Potential Risks and Challenges

Module 7: Prompt engineering and fine-tuning Fundamentals

  • Understanding Prompt Engineering
  • Significance of Prompts
  • Factors in Prompt Engineering

Module 8: Large Language Models

  • Introduction & Key characteristics
  • Pre-Training and Fine-Tuning

Module 9: Responsible AI

  • Introduction of Responsible AI
  • Why responsible AI is important
  • Design responsible AI
  • Best practices for responsible AI principles

Module 10: Variational Autoencoders (VAEs)

  • What are Variational Autoencoders (VAEs)
  • Architecture Variational Autoencoders
  • Advantages of VAEs
  • Challenges of VAEs
  • Applications of VAEs

Module 11: Training Generative Models

  • Building your own Foundation Model
  • Pre-training LLM
  • Fine-tuning LLM


This program is beneficial to professionals from a variety of industries and backgrounds. Those who are looking for a career into the field of GenAI, ML/DL, who have knowledge or prior experience in programming and mathematics, and an analytical frame of mind will be added advantage.


Beginners to GenAI, ML/DL.



Course Benefits

  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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