Admissions Open for JANUARY Batch
Discover the math powering transformers, embeddings, and scalable AI models.
Days : Tue & Thu
Duration : 10 Hours
Timings: 8 - 10 PM IST
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1 Months
5 Hours
Tue & Thu
LLM AGI Maths
This is the most advanced module that explains the math used in Large Language Models (like GPT) and frontier AI systems. You’ll learn how attention works, how data is represented as embeddings, how neural networks approximate functions, and how scaling rules guide building larger models.
Online Live Instructor-Led Learning
10 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Attention mechanisms and transformer math
Approximation and scaling law concepts
Get familier with
Embedding spaces and vector similarity
Information bottleneck representations
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners aiming to apply advanced mathematical concepts to large language models and generative AI, focusing on topics – transformers, embeddings, and sequence modeling for cutting-edge AI applications.
Prerequisites
Deep learning, optimization, and information theory mastery.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Deep learning, optimization, and information theory mastery.
Who Should Enroll?
This course is for learners aiming to apply advanced mathematical concepts to large language models and generative AI, focusing on topics - transformers, embeddings, and sequence modeling for cutting-edge AI applications.
By end of this course
Get Stronger in
- Attention mechanisms and transformer math
- Approximation and scaling law concepts
Get Familiar in
- Embedding spaces and vector similarity
- Information bottleneck representations
Course Contents
What is covered: Mechanisms for focusing on important parts of data (self-attention).
Application: Large language models (GPT),
translation, summarization.
Example:
1. Chatbots: Transformers generate human-like responses by attending to relevant words.
2. Text Summarization: Focusing on key sentences to create summaries.
What is covered: Representing data as vectors, measuring similarity.
Application: Semantic search, recommendation systems.
Example:
1.Search Engines: Finding similar documents using vector embeddings.
2.Product Recommendations: Suggesting items based on user preferences.
What is covered: Understanding how neural networks can approximate any function.
Application: Designing powerful models, theoretical ML research.
Example:
1. Universal Approximation: Proving that deep networks can learn complex patterns.
2. Model Scaling: Building larger models for better performance.
What is covered: Compressing data while keeping important information.
Application: Efficient model design, interpretability.
Example:
1. Mobile AI Apps: Compressing models for faster inference on phones.
2. Feature Selection: Keeping only the most useful features for predictions.
What is covered: Mathematical rules for training large models efficiently.
Application: Building and training models like GPT-4.
Example:
1. GPT Training: Using scaling laws to decide how much data and compute is needed.
2. Efficient AI Research: Optimizing resources for state-of-the-art models.
What is covered: Mechanisms for focusing on important parts of data (self-attention).
Application: Large language models (GPT),
translation, summarization.
Example:
1. Chatbots: Transformers generate human-like responses by attending to relevant words.
2. Text Summarization: Focusing on key sentences to create summaries.
What is covered: Representing data as vectors, measuring similarity.
Application: Semantic search, recommendation systems.
Example:
1.Search Engines: Finding similar documents using vector embeddings.
2.Product Recommendations: Suggesting items based on user preferences.
What is covered: Understanding how neural networks can approximate any function.
Application: Designing powerful models, theoretical ML research.
Example:
1. Universal Approximation: Proving that deep networks can learn complex patterns.
2. Model Scaling: Building larger models for better performance.
What is covered: Compressing data while keeping important information.
Application: Efficient model design, interpretability.
Example:
1. Mobile AI Apps: Compressing models for faster inference on phones.
2. Feature Selection: Keeping only the most useful features for predictions.
What is covered: Mathematical rules for training large models efficiently.
Application: Building and training models like GPT-4.
Example:
1. GPT Training: Using scaling laws to decide how much data and compute is needed.
2. Efficient AI Research: Optimizing resources for state-of-the-art models.
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities