Generative AI Course

999.00 (Inc. GST)

The Faculty Development Program is exclusively for professionals and faculty members and is not available to students; payment is non-refundable once processed.

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The Generative AI Course by IFACET, IIT Kanpur, is a comprehensive 15-hour program designed to provide intermediate-level learners with a deep understanding of generative AI and its applications. Over 1 month, participants will gain proficiency in designing and implementing generative AI models for various tasks, practical experience through real-world projects and case studies, and insight into the ethical challenges in generative AI. The course covers a wide range of topics, including deep learning, variational autoencoders, generative adversarial networks, reinforcement learning, and more.

Need for the Course:

The rapid advancements in AI technology have made generative AI one of the most exciting and impactful areas of research and application. As industries increasingly leverage AI for creative and innovative solutions, there is a growing demand for professionals skilled in generative AI techniques. This course addresses the gap by providing a thorough understanding of generative models, their practical implementations, and their potential applications across various sectors. It prepares participants to tackle complex real-world problems and contribute to the development of cutting-edge AI solutions.

Job Roles After Completing the Course

Upon successful completion of the Generative AI Techniques Course, participants will be well-equipped for various job roles, including:

  • Generative AI Engineer
  • Machine Learning Engineer
  • Data Scientist
  • AI Research Scientist
  • AI Solutions Architect
  • Computer Vision Engineer
  • Natural Language Processing (NLP) Engineer
  • Robotics Engineer

Benefits of this Program

  • Comprehensive Curriculum: The course offers a broad and deep coverage of generative AI techniques, from fundamental concepts to advanced applications, ensuring a well-rounded understanding.
  • Hands-on Learning: Emphasis on practical experience through hands-on projects, guided assignments, and real-world case studies, enabling participants to apply their knowledge effectively.
  • Expert Instructors: The course is taught by experienced professionals and academics from IIT Kanpur and IKIGAI School of AI, providing high-quality instruction and insights from leading experts in the field.
  • Certification: Participants receive a certification upon successful completion, validating their skills and knowledge in generative AI.
  • Flexible Learning Options: The course includes doubt sessions and master classes for self-paced learners, ensuring support and flexibility to accommodate different learning preferences.
  • Industry-Relevant Skills: The curriculum is designed to meet the current demands of the AI industry, preparing participants for roles that require cutting-edge generative AI expertise.
  • Ethical Considerations: The course includes discussions on the ethical challenges and considerations in generative AI, preparing participants to develop responsible and fair AI solutions.

Objective / Outcome Expected

  • Comprehensive understanding of generative AI techniques and their applications.
  • Proficiency in designing and implementing generative AI models for various tasks.
  • Practical experience through real-world projects and case studies.
  • Application of generative AI techniques to real-world problems.
  • Insight into reinforcement learning for generative tasks.
  • Awareness and consideration of ethical challenges in generative AI.

Target Audience

  • Ideal for individuals with a solid understanding of basic programming and machine learning concepts.
  • Suitable for data scientists, AI engineers, researchers, and professionals who want to specialize in generative AI.
  • Also beneficial for those interested in exploring advanced applications and cutting-edge trends in generative AI.

Key Features

  • Comprehensive coverage of generative AI techniques and applications.
  • Practical experience through hands-on projects and real-world case studies.
  • Certification upon successful completion.

Curriculum

Module 1: Introduction to Generative AI (2 hours)

  1. Overview of Generative AI
    • Definition and history
    • Applications of Generative AI in various fields
  2. Types of Generative Models
    • Generative Adversarial Networks (GANs)
    • Variational Autoencoders (VAEs)
    • Autoregressive Models

Module 2: Deep Learning Fundamentals (2 hours)

  1. Introduction to Neural Networks
    • Basics of neural networks
    • Training neural networks
  2. Key Deep Learning Concepts
    • Backpropagation
    • Activation functions
    • Optimization techniques

Module 3: Generative Adversarial Networks (GANs) (4 hours)

  1. Introduction to GANs
    • Concept and architecture
    • Training process of GANs
  2. Implementing GANs
    • Building GANs using TensorFlow/PyTorch
    • Hands-on exercise: Training a GAN on a simple dataset
  3. Advanced GANs
    • Conditional GANs
    • StyleGANs
    • CycleGANs

Module 4: Variational Autoencoders (VAEs) (3 hours)

  1. Introduction to VAEs
    • Concept and architecture
    • Difference between VAEs and traditional autoencoders
  2. Implementing VAEs
    • Building VAEs using TensorFlow/PyTorch
    • Hands-on exercise: Training a VAE on an image dataset
  3. Applications of VAEs
    • Image generation
    • Anomaly detection

Module 5: Autoregressive Models (2 hours)

  1. Introduction to Autoregressive Models
    • Concept and architecture
    • Examples of autoregressive models (PixelRNN, PixelCNN)
  2. Implementing Autoregressive Models
    • Building autoregressive models using TensorFlow/PyTorch
    • Hands-on exercise: Training an autoregressive model

Module 6: Practical Applications and Ethical Considerations (2 hours)

  1. Real-world Applications of Generative AI
    • Art and content creation
    • Data augmentation
    • Drug discovery
  2. Ethical Considerations
    • Bias in generative models
    • Deepfakes and their implications
    • Guidelines for responsible use of Generative AI

Module 7: Mini Project (2 hours)

  1. Project Overview
    • Description of the mini project
    • Project requirements and deliverables
  2. Implementation and Presentation
    • Students work on their projects
    • Presentation of the projects and feedback

Prerequisites

  • Solid understanding of basic programming and machine learning concepts.
  • Recommended prior knowledge in AI technologies.
  • Suitable for data scientists, AI engineers, researchers, and professionals looking to specialize in generative AI.
  • Familiarity with mathematics is beneficial but not mandatory.