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Generative AI Crash Course with Hands-on Implementations

3,315.00

You will find this course added to your account after the purchase.

ADaSci Members receive 50% discount.

Description

Agenda

Generative AI is trending because of the latest interesting developments in the field. From DALL-E to Diffusion models to ChatGPT, there have been many such models introduced to this field in just two years that have surprised people with how AI can perform better than humans in many fields of work. Now the practitioners interested in AI are eagerly looking to advance their skills in Generative AI.

ADaSci presents this course to introduce Generative AI to the community and keep the practitioners updated with this latest trend. This Generative AI course covers a detailed explanation of Generative AI and popular models used in it with their working and real-world implementations of popular Generative AI models.

 

Learning Outcomes

  • In-depth understanding of Generative AI and its popular models.
  • Detailed knowledge of GPT models, Diffusion models, different NLP transformers and ChatGPT.
  • Hands-on knowledge of implementing Generative AI models in real-world applications.

 

Contents

  1. Introduction to Generative AI
    1. Overview and Developments
    2. Discriminative AI vs Generative AI
    3. Popular models and applications
  2. Generative AI in Computer Vision
    1. Generative Adversarial Networks (GANs)
    2. DALLE
    3. Stable Diffusion
  3. Generative AI in NLP
    1. GPT and Its Variants
    2. Other Transformers in NLP
    3. ChatGPT
  4. From GANs to ChatGPT

 

Prerequisite

  • Basic understanding of artificial intelligence, machine learning and deep learning.
  • Good knowledge of Python programming language.
  • Familiarity with Jupyter/Colab Notebook environment

 

Requirements

  • Jupyter Notebook / Google Colab
  • High-Speed Internet connectivity

 

Instructor

Dr. Vaibhav Kumar | Senior Director, Association of Data Scientists (ADaSci)

Dr. Vaibhav Kumar is a seasoned data science professional with great exposure to machine learning and deep learning. He has good exposure to research, where he has published several research papers in reputed international journals and presented papers at reputed international conferences. He has worked across industry and academia and has led many research and development projects in AI and machine learning. Along with his current role, he has also been associated with many reputed research labs and universities where he contributes as visiting researcher and professor.