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Mastering Generative AI: Innovations and Applications
Unlock the Power of Generative AI
Immerse yourself in the dynamic realm of Generative AI with our specialized course, designed to unravel the complexities of advanced models such as Variational Autoencoders (VAEs), Transformers, Large Language Models, and technologies like Stable Diffusion. This comprehensive course offers deep insights into the foundational algorithms that drive these models, their design principles, and a plethora of real-world applications, equipping you to harness the full potential of Generative AI technologies. Gain an in-depth understanding of how these models can generate text, images, and other forms of media, transforming the landscape of digital content creation.
PREREQUISITES:
Proficiency in Python, Machine Learning, Deep Learning, and NLP is essential for this course
Advanced Generative Course Curriculum (Syllabus)
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Module 1: Introduction To Artificial Intelligence
1. Introduction to AI
2. AI vs ML vs DL
3. Types of learning (Supervised, Unsupervised & Reinforcement)
4. Core Difference between ML and DL
5. Life Cycle of ML and DL Project
Module 2: Introduction To Generative AI
1. Introduction to Generative AI
2. Overview of generative AI technologies.
3. Applications and case studies across industries.
Module 3: Getting Started With Large Language Models
1. Into to large language Models
2. History of NLP
3. Intro to RNN,LSTM,GRU
4. Intro to Encoder Decoder Model
Module 4: Prompt Engineering And Working With LLM
1. Intro to Prompt Engineering
2. LLM with Prompt Engineering
3. Introduction to GPT models.
4. Understanding how GPT-3 and GPT-4 work
5. Training on popular LLMs like GPT (Generative Pre-trained Transformer).
6. Practical applications of LLMs in generating text, code, and more
Case Study: Creating a project with LLMS
Module 5: Working with Open AI API
1. Intro To Open Ai
2. Utilizing OpenAI APIs
3. Setting up and authenticating API usage.
4. Practical exercises using GPT-3/GPT-4 for text generation.
5. Understanding DALL-E and its capabilities in image generation.
📜Hands-on project to generate images from textual descriptions.
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Module 6: Working with Google Gemini Gen API
1. Getting Started with Gemini
2. How to obtain an API key for Gemini.
3. Overview of the Gemini API and accessing its features.
4. Detailed exploration of different Gemini models.
5. Selecting and initializing the right model for specific tasks.
6. Step-by-step project to create an AI-powered chatbot using Gemini.
Module 7: Working with Meta’s LLaMA API
1. Introduction of LLaMA .
2. Comparison with other large language models like GPT-3 and GPT-4.
3. Key features and capabilities of LLaMA
4. Understanding the Model Architecture of LLaMA.
5. Discussion on model sizes and capabilities.
6. Environment setup: Installing necessary libraries and tools.7. Intro to the architecture of LLaMA models
8. Understanding the differences between LLaMA model variants (8B, 13B, 30B,
and 70B parameters)
9. Implementing text generation using LLaMA
Module 8: Working With Hugging Face Ecosystem
1. Introduction to the Hugging Face ecosystem and the Transformers library.
2. Exploring Hugging Face Models and Tokenizers.
3. Project:
4. Introduction to the Trainer API.
5. Integrating Hugging Face models with web application
Module 9: Building Gen AI Apps Using Lang Chain
1. Introduction to the LangChain framework
2. Understanding the purpose and core components of LangChain Framework
3. LangChain Setup and necessary dependencies
4. Basic configuration and setup for development
5. Step-by-step guide
Module 10: Intro To RAG
1. Intro To RAG
2. Building applications using RAG
3. LLMs in Depth
4. Fine Tuning LLMs
5. Training LLMs by Implementing Fine Tuning
Module 11: Stable Diffusion by Stability AI
1. Intro to Stable Diffusion
2. Fundamentals of Diffusion Models
3. Application of Stable Diffusion
4. Modifying image attributes and styles using prompt engineering
5. Parameters of image generation: seeds, prompts, and steps explained
6. Tool For Stable Diffusion
7. Fine-tuning and training Stable Diffusion on custom datasets
8. Advanced prompt engineering and achieving specific artistic effects.
9. Introduction to variations and derivatives of Stable Diffusion (e.g., DreamBooth
for personalization).
10.Using the Diffusion library for more control over the diffusion process.
11. Integrating Stable Diffusion models into web applications
12.Advance Stable Diffusion Techniques
Benefits/Opportunities
- Master advanced AI concepts
- Build your own AI applications
- Contribute to AI research
- Explore creative possibilities
Learn the latest and most advanced ideas in AI, like how computers can create new content or improve existing data
Create your own tools and programs using AI technology. You can make things like new articles, and videos, or even help improve data quality.
Join the forefront of AI innovation, where you can explore new possibilities and help shape the future of technology. Your contributions could lead to groundbreaking discoveries in AI.
Experiment with AI to unlock your creativity. Whether it’s generating art, music, or stories, AI offers endless opportunities for creative expression and exploration.
Relevant Job Roles/Titles
GenAI Engineer
AI Application Developer
Prompt Enginner
Machine Learning Engineer
AI Research Scientist
NLP Engineer
Deep Learning Engineer
Computer Vision Engineer
GLIMPSES OF OUR VICTORY – Successfully Placed Innominions
Here are the Success Stories of our Innominions
Frequently Asked Questions (FAQs)
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What will I learn in Generative AI Course?
In Generative AI course, you will learn how to find valuable data, analyze and apply mathematical skills to it to use in business for making great decisions, developing a product, forecasting, and building business strategies.
What is the average salary of a AI Engineer?
The salary of an AI engineer varies based on their skillset. Recent reports suggest that, on average, AI engineers earn ₹14,00,000 per year.
Are there any prerequisites to learn the Generative AI?
One need not have any major knowledge in Data Science. Proficiency in Python, Machine Learning, Deep Learning, and NLP is essential for this course
What are my takeaways after completion of the Generative AI course?
Based on the program you choose, you will get a course completion certificate from Innomatics.
What are the career opportunities in Generative AI Technology?
As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.
Here are some of the roles you can find in Data Science
- GenAI Engineer
- AI Application Developer
- Prompt Engineer
- NLP Engineer
- Machine Learning Engineer
- AI Research Scientist
- Deep Learning Engineer
- Computer Vision Engineer