Synergefic | Generative AI

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.

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.

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.

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)

Testimonials

Synergific Software Team has been very supportive, and working with them has been a best decision that we could ever made, They are just a call away. You guys are AWESOME, Thank You, Keep up the Good Work!!!

Shamsudeen Bawa

Vice President, J.P Morgan, CIS, USA

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

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

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

Generative AI

Overview

Objectives

Prerequisites

Course Outline

Testimonials

The Synergific Training Advantage

Expert Instructors

Hands-On CloudLabs

Certification Vouchers

Flexible Scheduling

Frequently Asked Questions

Generative AI

Overview

Objectives

Prerequisites

Course Outline

Module 1: Machine Learning and Industry use cases

Module 2: Introduction to Deep Learning & Tensor Flow

Module 3: Introduction to CNN & RNN

Module 4: Generative Adversarial Networks with Python

Module 5: Generative Adversarial Networks

Module 6: Sentiment Analysis

Module 7: Fundamentals of AI, Computational needs of AI & Google Dialogflow, Nuance

Module 8: Course Conclusion and Q&A

Testimonials

The Synergific Training Advantage

Expert Instructors

Hands-On CloudLabs

Certification Vouchers

Flexible Scheduling

Frequently Asked Questions