Deep Learning for Generative AI: A Comprehansive Foundation

From AI fundamentals to building Transformers in PyTorch — gain the skills to create, innovate, and lead in the AI revolution with Prutor AI, developed at IIT Kanpur.

15- Days | 15+ hours of learning | Training by best Industry Experts

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Why Learn Generative AI Course

The demand for Generative AI professionals is growing rapidly as organizations across industries embrace AI-powered solutions and innovative technologies. With the exponential rise of AI applications in business, creative fields, and technology sectors, there is a significant gap between the demand for skilled AI professionals and the available talent pool. This shortage has created numerous high-paying job opportunities for Generative AI experts.

Overview

Generative AI refers to artificial intelligence systems that can create new content, including text, images, music, code, and other data types by learning patterns from existing data and generating novel outputs that maintain similar characteristics and quality.

This instructor-led, live training (online or onsite) is aimed at developers, data scientists, and professionals who wish to understand and implement generative AI solutions for various applications.

By the end of this training, participants will be able to:

  • Implement generative AI systems and models
  • Choose the most suitable algorithms, frameworks, and approaches
  • Understand different generative AI architectures (GANs, Transformers, Diffusion Models)
  • Create and train generative models for various applications
  • Apply best practices for working with generative AI
  • Handle ethical considerations and limitations of generative systems

Generative AI Course: 15-Days Plan

Day 1: Introduction to AI & Generative AI
Day 2: Machine Learning & Deep Learning Fundamentals
Day 3: Linear Algebra - Vectors & Matrices
Day 4: Linear Algebra - Special Matrices & Transformation
Day 5: Probability Theory - Basics & Distributions
Day 6: Statistics - Data Understanding & Inference
Day 7: Calculus - Optimization & Gradient Descent Introduction of Calculus
Day 8: Neural Network and Deep Learning Basics
Day 9: Introduction to Generative Models & GANs Basics
Day 10: GANs - Deep Dive & Conceptual Implementation
Day 11: Autoencoders & Introduction to VAEs
Day 12: VAEs - Deep Dive & Conceptual Implementation
Day 13: Autoregressive Models - Statistical & Early Deep Learning
Day 14:Introduction to Transformers
Day 15: Transformers - Attention & Conceptual Implementation

What You Will Get from This Program