Duration: Five Days


In this Generative AI course, students learn the fundamentals of deep learning and how to train generative AI models, starting with a review of core Python concepts if needed. You will also learn about the Anaconda computing environment, importing and manipulating data with Pandas, and exploratory data analysis with Pandas and Seaborn.


  • Understand the basics of machine learning (ML) and deep learning, including the different types of ML models, classification and regression, and neural networks.
  • Develop a deep learning model construction for prediction, including preprocessing tabular datasets for deep learning workflows, data validation strategies, architecture modifications for managing overfitting, and regularization strategies.
  • Apply trustworthy AI frameworks for this deep learning prediction context.
  • Learn the fundamentals of generative AI, including generating new content versus analyzing existing content, example use cases, and the ethics of generative AI.
  • Implement sequential generation with recurrent neural networks (RNNs) and variational autoencoders (VAEs).
  • Build a generative adversarial network (GAN).
  • Understand transformer architectures, including the problems with recurrent architectures, attention-based architectures, positional encoding, and the Transformer model.
  • Gain an overview of current popular large language models (LLMs), such as ChatGPT, DALL-E 2, and Bing AI.
  • Learn about medium-sized LLMs that can be run in your environment, such as Stanford Alpaca and Facebook Llama.
  • Explore transfer learning with your data in these contexts.


Learners should have prior experience developing Deep Learning models, including architectures such as feed-forward artificial Neural Networks, recurrent and convolutional. 

Outline for Fundamentals of Deep Learning and Development of Generative AI Models Training

  • Review of Core Python Concepts (**if needed – depends on tool context**)
    • Anaconda Computing Environment
    • Importing and manipulating Data with Pandas
    • Exploratory Data Analysis with Pandas and Seaborn
    • NumPy ndarrays versus Pandas Dataframes
  • Overview of Machine Learning / Deep Learning
    • Developing predictive models with ML
    • How Deep Learning techniques have extended ML 
    • Use cases and models for ML and Deep Learning 
  • Hands on Introduction to Artificial Neural Networks (ANNs) and Deep Learning 
    • Components of Neural Network Architecture
    • Evaluate Neural Network Fit on a Known Function
    • Define and Monitor Convergence of a Neural Network
    • Evaluating Models
    • Scoring New Datasets with a Model
  • Hands on Deep Learning Model Construction for Prediction 
    • Preprocessing Tabular Datasets for Deep Learning Workflows
    • Data Validation Strategies
    • Architecture Modifications for Managing Over-fitting
    • Regularization Strategies
    • Deep Learning Classification Model example
    • Deep Learning Regression Model example 
    • Trustworthy AI Frameworks for this DL prediction context
  • Generative AI fundamentals:
    • Generating new content versus analyzing existing content
    • Example use cases: text, music, artwork, code generation
    • Ethics of generative AI
  • Sequential Generation with RNN
    • Recurrent neural networks overview
    • Preparing text data
    • Setting up training samples and outputs
    • Model training with batching
    • Generating text from a trained model
    • Pros and cons of sequential generation
  • Variational Autoencoders
    • What is an autoencoder?
    • Building a simple autoencoder from a fully connected layer
    • Sparse autoencoders
    • Deep convolutional autoencoders
    • Applications of autoencoders to image denoising
    • Sequential autoencoder
    • Variational autoencoders
  • Generative Adversarial Networks
    • Model stacking
    • Adversarial examples
    • Generational and discriminative networks
    • Building a generative adversarial network 
  • Transformer Architectures
    • The problems with recurrent architectures
    • Attention-based architectures
    • Positional encoding
    • The Transformer: attention is all you need
    • Time series classification using transformers
  • Overview of current popular large language models (LLM):
    • ChatGPT
    • DALL-E 2
    • Bing AI
  • Medium sized LLM on in your own environment:
    • tanford Alpaca
    • Facebook Llama
    • Transfer learning with your own data in these contexts 


01/15/2024 - 01/19/2024
10:00 AM - 06:00 PM
Eastern Standard Time
Online Virtual Class
USD $2,995.00
02/19/2024 - 02/23/2024
10:00 AM - 06:00 PM
Eastern Standard Time
Online Virtual Class
USD $2,995.00
03/25/2024 - 03/29/2024
10:00 AM - 06:00 PM
Eastern Standard Time
Online Virtual Class
USD $2,995.00