Generative AI

Live Online (VILT) & Classroom Corporate Training Course

Master the art of creating AI models for creative outputs with the Generative AI course. Learn about generative models like GANs and VAEs, explore applications in image synthesis, text generation, and music composition, and unleash your creativity with generative AI.
Generative AI

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Overview

The “Generative AI” course provides a comprehensive understanding of generative artificial intelligence models and techniques. Participants will learn about the underlying principles of generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), and explore various applications of generative AI in image synthesis, text generation, music composition, and more.

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Objectives

At the end of Genrative AI course, participants will be able to

  • Gain a deep understanding of generative AI models and their applications.
  • Learn about popular generative models like GANs and VAEs.
  • Explore techniques for image synthesis, text generation, and music composition using generative AI.
  • Understand evaluation metrics and approaches for assessing generative models.
  • Apply generative AI techniques in real-world projects and creative endeavors.
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Prerequisites

  • Basic knowledge of machine learning concepts and algorithms.
  • Familiarity with programming languages such as Python.
  • Understanding of neural networks and deep learning principles.
  • Prior experience with popular deep learning frameworks like TensorFlow or PyTorch is beneficial but not mandatory.
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Course Outline

  • Machine Learning and Industry use cases
    • ML use in semiconductor Industry
    • ML use in Chip Design
  • Unsupervised Learning & Cluster Analysis
    • Supervised vs unsupervised learning
    • Need of Cluster Analysis
    • K- Means clustering algorithm
    • The theory behind cluster Analysis
    • Building and interpreting clusters
  • Decision Trees
    • Segmentation
    • Entropy
    • Information gain
    • Building Decision Trees
    • Validation of Trees
    • Pruning the trees
    • Fine tuning the trees
    • Prediction using Trees
    • Customer retention case study
  • Random Forest
    • Ensemble Learning
    • Ensemble Models
    • Bagging
    • Boosting

  • Introduction to Deep Learning
    • Overview of Natural Language Processing
      o Machine learning methods
      o Deep learning methods.
  • Tensorflow
    • Introduction to TensorFlow
    • Installation & Setup
    • TensorFlow Basic Syntax
    • TensorFlow Graphs
    • Variables and Placeholders
  • TensorFlow Graph & TF Data
    • Building the GRAPH
    • Running a session
    • Data augmentation
    • Tensor flow optimization
  • Keras
    • What is keras
    • Building models with Keras
    • Understanding various features of Keras
  • Artificial Neural Networks
    • ANN Intuition
    • Building an ANN
    • Homework Challenge – Should we say goodbye to that customer?
    • Evaluating, Improving and Tuning the ANN
    • Homework Challenge – Put me one step down on the podium

  • Convolutional Neural Networks
    • CNN Intuition
    • Homework – What’s that pet ?
    • Evaluating, Improving and Tuning the CNN
  • Recurrent Neural Networks
    • RNN Intuition
    • Building an RNN
    • Evaluating, Improving and Tuning the RNN

  • Introduction to GAN
    • Deriving the MiniMax Loss Function
    • Analysis of Least Squares Loss Function
    • GAN Architecture Used
    • Coding the Generator and Discriminator Architecture
    • Function and GAN Estimator
    • Modifying GAN Framework to Change Dataset and Loss Function
  • The generative revolution: coming home
    • The present and future of AI is generative
    • Applications of generative AI

  • Benefits and possibilities of Generative AI
    • Understanding the battle between generator and discriminator
    • Understanding Cross Entropy in depth
    • Understanding the equation to calculate the discriminator loss
    • Understanding the equation to calculate the generator loss
  • GAN’s – Coding
    • Coding: importing libraries and declaring a visualization function
    • Coding: hyperparameters and the DataLoader
    • Coding: the generator class
    • Coding: the discriminator class
  • Coding an advanced generative architecture
    • Challenges and issues of the basic GAN
    • The Wasserstein Loss
    • The Gradient Penalty
    • Coding: setting up libraries and parameters
    • Coding: Login and setup of the Wandb stats library
    • Coding: Beginning the generator
    • Coding: Understanding convolutions
  • Generating images from text by combining two advanced architectures
    • Multimodal generation, an incredible adventure
    • Coding: importing the libraries
    • Coding: helper functions and hyperparameters
    • Coding: Setting up the CLIP model
    • Coding: Setting up the Generative transformer model
    • Coding: Setting up the latent space parameters to be optimized

  • Sentiment Analysis
    • Understanding sentiment Analysis
    • Sentiment analysis Hands on using twitter Data

  • Introduction to Artificial Intelligence & Machine Learning
    • Introduction to Machine Learning
    • Machine Learning tools and Techniques
  • Introduction to Chatbots
    • Understanding the transactional chatbots
    • Understanding the informational chatbots
  • NLTK Introduction & Installation
    • Sentence Splitter & Tokenization
    • Stemming & Lemmatization
    • Stop work removal
    • sentiParts of Speech (POS) tagging
    • Chunking
  • Google Dialog flow
    • The Big Picture
    • Introducing DialogFlow
    • The Big Picture
    • Setting Up Dialogflow
    • Building Blocks of Interaction Models
    • Creating Your First Agent
    • Exploring Agent Settings
    • Default Intents
    • Smalltalk
    • Custom Intents
    • System Entities and Developer Entities
    • Defining Developer Entities
    • User Expressions for Intents
  • Configuration of Dialogflow
    • Configuring and Testing the Book Cars Intent
    • Configuring and Testing the Book Rooms Intent
    • Linear and Non-linear Dialogs
    • Section Overview
    • Contexts
    • Follow up Intents
    • Linear Dialogs
    • Non-linear Dialogs
    • Non-linear Dialogs continued
    • Fulfilment, Deployment and 3rd Party Integration
  • Setting up Agent
    • Section Outline
    • Check Weather Intent
    • Basic Setup of Webhook Code
    • Extracting Parameter Values and Structuring Responses
    • Calling the Open Weather Map API
    • Retrieving Weather Info from Open Weather Map
  • Building
    • Building: Intents and Entities
    • Building: Dialog
    • Deployment and testing
  • Advanced chatbot improvement
    • Context Variables
    • Improvement Tab
  • Nuance – AI
    • Intents
    • Entities
    • Dialog
    • Mix Dash board
    • Adding Intents
    • Adding Entities
    • Linking Entities
    • Train & Try
    • Dialog Greeting
    • Intent Mapper
    • Intent Components
    • Recovery Messages
    • Node Types
    • Grammars
    • Variables

  • Couse conclusion
  • Reference books, videos and blogs
  • Next steps
  • Final Q&A
  • Final assessment (optional)
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Testimonials

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Shamsudeen Bawa

Vice President, J.P Morgan, CIS, USA

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Synergific Software has been of great help and I plan to continue to use your services in the future for my business needs.

Farhan Hafiz

Data Architect, Fiserv

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I think Synergific Software is great. I liked that it was hassle free and easy to set up. Again, it's a great feature for a fast and cheap set up, which gives me peace of mind, as I know have a terms of use agreement.

Dr. Sahdev Singh

Under Secretary, Ministry of Law & Justice, Govt. of India

M Chikanna Swamy

I liked using Synergific Software very much. I thought the website was easy to navigate and the instructions for generating the terms was clear. I even recommended you on a Facebook Group I am a member of.

M Chikanna Swamy

Director & Learning Head, Mindtree