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Foundations of Generative AI: Exploring Generative Models

Course Summary

This course offers a comprehensive learning experience for developers, data engineers/analysts, and tech product owners. The course is designed to equip participants with the essential skills and in-depth knowledge required to harness the power of generative AI effectively. By combining theory with extensive hands-on practice, this course ensures that participants gain a deep understanding of generative AI concepts and the ability to apply them to various domains. Students will learn how to generate realistic and novel outputs, such as images, music, text, and more, using state-of-the-art algorithms and frameworks.

Purpose
Gain a deep understanding of Generative AI concepts and the ability to apply them.
Prerequisites

Participants should have a solid understanding of Python programming, including knowledge of data structures, control flow, functions, and libraries commonly used in data analysis and machine learning, such as NumPy, Pandas, and scikit-learn.

Participants should have working knowledge of data analysis concepts, exploratory data analysis (EDA), and machine learning algorithms

Basic knowledge of deep learning concepts

Audience
Data Engineers/Analysts | Developers | Tech Product Managers
Skill level
Intermediate
Style
Lecture | Hands-on Activities 
Duration
3 days
Related technologies
Python | Deep Learning

 

Productivity objectives
  • Understand Gen AI and generative models
  • Create realistic outputs such as images and text using state-of-the-art algorithms and frameworks
  • Generate embeddings and vector databases

What you'll learn:

In this course, you'll learn:
  • Overview of Generative AI
    • Introduction to Generative AI and its applications
    • Understanding the basics of generative models and their importance
    • Overview of different types of generative models (e.g., GNAs, VAEs, autoregressive Models)
  • Deep Learning Primer
    • Recap of essential deep learning concepts
    • Review of neural networks and their architectures
    • Explanation of optimization techniques (e.g., gradient descent, blackpropagation)
  • Building Blocks of Generative Models
    • Understanding probability distributions and sampling techniques
    • Introduction to latent space and representation learning
    • Hands-On exercise: Implementing a simple generative model using Python and TensorFlow/PyTorch
  • Variational Autoencoders (VAEs)
    • VAEs Architecture
    • Training VAEs and generating new samples
    • Hands-On Exercise: Building a VAE for image generation and reconstruction
  • Generative Adversarial Networks (GANs)
    • Exploring the theory behind GANs
    • GAN architecture and training process
    • Generating Synthetic Data using GANs
    • Hands-On Exercise: Building a VAE for image generation and reconstruction
  • Advanced Generative Models
    • Introduction to Autoregressive Models (e.g., PixelCNN, WaveNet)
    • Discussion on flow-based models (e.g., Glow, RealNVP)
    • Hands-on exercise: Implementing an autoregressive model for text generation
  • Text Generation
    • Techniques for generating text using generative models
    • Text generation with recurrent neural networks (RNNs) and transformers
    • Hands-on exercise: building a text generation model using RNN’s or transformers
  • Generating Embeddings
    • Introduction to Embeddings
    • Embedding Techniques
    • Sentence/Document Embeddings
  • Vector Databases
    • Introduction to Vector Databases
    • Building Vector Databases
  • Project
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

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