Generative AI Course Training in Toronto, Canada

• What is NLP?
• History of NLP
• NLP End to end workflow
• Stopwords
• Tokenization
• Stemming
• Lemmatization
• POS tagging
• TFIDF

• One hot encoding
• Bag of words
• Unigram
• Bigram
• ngram
• Word embeddings Skip Gram
• Word2vec model

• RNN
• LSTM Models & GRU Models
• Transfer learning

• Encoder-decoder architecture
• Attention mechanism
• Transformer
• BERT

• Hands-on experience with text translation using the encoder-decoder architecture

• LLM
• Use Cases
• Text Generation
• Chatbot Creation
• Foundations of Generative Models & LLM
• Generative Adversarial Networks (GANs)
• Autoencoders in Generative AI
• Significance of Transformers in AI
• "Attention is All You Need" - Transformer Architecture
• Reinforcement Learning
• RLHF

• Encoder Models i.e.
• BERT
• Decoder Models GPT
• Encoder Decoder Model i.e.
• T5

• Real-world applications and case studies of LLMs

• Instruction fine-tuning
• Fine-tuning on a single task
• Multi-task instruction fine-tuning
• Model evaluation
• Benchmarks
• Parameter efficient fine-tuning (PEFT)
• PEFT techniques 1: LoRA
• PEFT techniques 2: Soft prompts
• Lab 2 walkthrough

• Rouge1
• BLEU
• Meteor
• CIDEr

• Reinforcement Learning
• LLM Applications

• Deployment Strategies
• Hardware Requirements

• Langchain: A Framework for LLMs

• LLM Operations
• Scalability
• Best Practices

• Hugging Face
• GCP and Hugging Face Overview

• In-depth GCP
• Model Evaluation
• Prompt Design

• Azure ML
• Azure Cognitive Services
• Azure Databricks

• AWS Sagemaker
• AWS Jumpstart
• AWS Bedrock

• Responsible AI
• Google's Approach
• Ethical Issues

• Foundations of NLP
  • Introduction to NLP
  • Key Concepts and Terminologies
  • NLP Techniques and Algorithms
• Chatbots and Their Evolution
  • Definition of Chatbots
  • Evolution of Chatbots
  • Types of Chatbots

• Introduction to OpenAI and LLMs
  • Introduction to OpenAI and LLMs
  • What are LLMs?
  • How do LLMs work?
  • Types of LLMs
  • Practical uses of LLMs

• Introduction to GPT and ChatGPT
  • Overview of GPT
  • ChatGPT Capabilities
  • GPT Architecture
• Understanding GPT-3, GPT 3.5, and GPT-4
  • GPT-3 vs GPT-4
  • Advancements in GPT-4
  • Ethical Considerations

• Setting Up the ChatGPT Environment
  • Accessing OpenAI API
  • API Keys and Rate Limits
  • Setup for Development
• Building a Simple Chatbot with ChatGPT
  • Conversation Flows
  • GPT in Chatbots
  • Testing and Iteration
• Training and Fine-tuning ChatGPT
  • Transfer Learning
  • Pre-training and Fine-tuning ChatGPT
  • Data for Training
  • Dataset Preparation
  • Fine-tuning Techniques
  • Model Performance Monitoring
• Integrating ChatGPT with Other Services
  • Webhooks and APIs
  • Integration with Platforms
  • Chatbots for Social Media
• Advanced Conversation Design
  • Context and Long Conversations
  • Personality and Tone
  • Advanced Scripting
• RLHF and ChatGPT
  • Reinforcement Learning Principles
  • Human Feedback in Training
  • Role of RLHF in GPT

• ChatGPT for Business Applications
  • Customer Service Automation
  • Personal Assistants
  • Sales and Marketing Bots

• Safety and Ethical Considerations
  • Bias Detection and Mitigation
  • Ethical AI Use
  • Safety Measures

• The Future of ChatGPT and Conversational AI
  • Trends and Predictions
  • Potential Upgrades
  • Future of AI and Society
• Future of Chatbots and Conversational AI
  • Beyond ChatGPT: The next frontier
  • Opportunities and challenges

• Understanding AI: Descriptive vs Generative AI
  • The nature of AI
  • Comparison of descriptive and generative AI
• Introduction to Natural Language Processing
  • Core concepts in NLP
  • Basics of language understanding
• Understanding Large Language Models (LLMs)
  • Overview of LLMs
  • Their scope
  • Capabilities
  • Use cases
• Introduction to GPT & Chat GPT
  • What is GPT
  • Its evolution
  • Generational changes

• The Fundamentals of Prompt Engineering
  • What is prompt engineering
  • Its importance
  • Types of prompts
• Content Generation with Prompts
  • Strategies for generating text
  • Video scripts
  • Music using prompts
• Tokens and Parameters in AI
  • The role and understanding of tokens
  • Introduction to prompt parameters

• Zero-Shot to Few-Shot Learning
  • Deep dive into zero-shot
  • One-shot
  • Few-shot learning
• Fine-Tuning AI Model Parameters
  • Introduction to model parameter adjustments
• Hallucinations and Bias in AI
  • Strategies for managing AI hallucinations and biases
• Advanced Prompt Engineering Techniques
  • Methods for crafting complex prompts
  • Incorporating creativity and context
• Refining and Optimizing Prompts
  • Techniques for prompt refinement and iterative improvement

• Metrics for Evaluating Prompts
  • How to assess prompt quality and performance
• Human Evaluation of Prompts
  • Techniques for collecting and analyzing human feedback on prompts
• Testing Prompts on Different Models and Tasks
  • How to assess prompt performance across different AI models and tasks

• Natural Language Processing
  • Question-Answering Systems
  • Conversational AI
  • Sentiment Analysis
  • Text Summarization
• Code Generation with Prompt Engineering
  • GitHub Copilot Exploration
• Image & Video Content Creation using prompt engineering
  • Using Midjourney and other tools. DALL-E 2 and GPT-4: A Comprehensive Overview Exploring the capabilities and limitations of DALL-E 2 and GPT-4 Real-world scenarios, case studies, tool-specific tips
• Music Generation with Prompt Engineering
  • Create Poem
  • Music

• Ethics & Bias in Prompt Engineering
  • Ethical considerations
  • AI transparency
  • Responsible AI usage

Introduction

• Python Introduction - Programing Cycle of Python
• Python IDE and Jupyter notebook

Variables

• Variables
• Data type

Code Practice Platform

• create , insert , update and delete operation , Handling erros

Operators

• Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
• Decision making - Loops

Loops

• While loop, for loop and nested loop
• Number type conversion - int(), long(). Float ()
• Mathametical functions , Random function , Trigonometric function

Sting

• Strings- Escape char, String special Operator , String formatting Operator
• Build in string methods - center(), count()decode(), encode()

List

• Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
• Built in Function - cmp(), len(), min(), max(), list comprehension

Tuples

• Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
• Built in tuples functions - cmp(), len ()

Dictionary

• Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
• Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
• Date & time -Time Tuple , calendor module and time module

Function

• Function - Define function , Calling function
• pass by refernece as value , Function arguments , Anonymous functions , return statements
• Scope of variables - local & global , Decorators and recursion
• Map reduce and filter

Modules

• Import statemnts , Locating modules - current directory , Pythonpath
• Dir() function , global and location functions and reload () functions , Sys module and subprocess module
• Packages in Python

Files

• Files in Python- Reading keyboard input , input function
• Opening and closing files . Syntax and list of modes
• Files object attribute- open , close . Reading and writing files , file Position.
• Renaming and deleting files
• Pickle and Json

Directories

• mkdir methid, chdir () method , getcwd method , rm dir

Exception Handling

• Exception handling - List of exceptions - Try and exception
• Try- finally clause and user defined exceptions

OOP

• OOP concepts , class , objects , Inheritance
• Overriding methods like _init_, Overloading operators , Data hiding

Regular Expressions

• match function , search function , matching vs searching
• Regular exp modifiers and patterns

SQLite and My SQL

• Data base connectivity
• Methods- MySQL , oracle , how to install MYSQL , DB connection
• create , insert , update and delete operation , Handling erros

Framework

• Introduction to Django framwork , overview , environment
• Apps life cycle , creating views
• Application, Rest API

Intro to Cloud Computing

• What is cloud computing, why it is important, cloud services, applications, benefits , architectures

Intro to Azure Cloud Platform

• What is Azure, Why Azure, Azure services, Azure core architecture, core azure services domains, creation of azure account

Azure Cloud Applications

• Intro to AI/ML services, What is azure ml designer studio, developing ml models, python and r applications in studio

Azure Cloud Services

• Resource groups, virtual machine concepts , storage service, web apps, databricks environment , azure sql databases, billing etc.

Azure open AI Studio

• What is azure open ai, open ai documentation, how to use azure open ai studio, creating applications, different models in azure open ai