Genai Foundation (by 3aayaam) - Maths Of Attention MechanismPublished 4/2026
Created by Soumyadeep Dey
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 70 Lectures ( 11h 57m ) | Size: 5 GB
What you'll learn
✓ Generative AI (GenAI) concepts
✓ Machine Learning (ML) concepts needed for GenAI development
✓ Hands On development using models from OpenAI (GPT) and Google (Gemini 3)
✓ Projects using GenAI development
Requirements
● No pre-requisites.
Description
In this course I will be explaining the pre-requisites for my GenAI vol 2 course "GenAI Continuum - Application Development using OpenAI & Gemini" . This course covers mathematical concepts, terminologies, jargons that are needed for deep understanding of GenAI (Foundation) Models.
This course is divided into 4 sections as mentioned below:
• GenAI Prequel - AI, ML & GenAI; Type & Nature of data; Scalars & Vectors; Mathematical equations in AI Models.
• The Original ML Story - Model Training; Gradient Descent; Types of ML Models; Underfitting & Overfitting; Deep Neural Networks; Epoch, Batch etc.
• Arrival of Transformer - Transformer Architecture; Tokenization; Vector Embedding; Vocabulary; Self-Attention & Masked-Attention; Encoder & Decoder.
• Nerd Attack - Mathematics of Self-Attention & Masked-Attention.
NOTE : This is an optional pre-requisite course for Application Development using GenAI. But I would recommend everyone to go through this if you want to understand the mathematics behind transformer architecture, which is the foundation of current GenAI models.
If you go through this course and understand the concepts, it will be easier for you to grasp the inner workings of GenAI and Foundation Models.
This is the first volume of my GenAI series of courses. Vol 2 will contain Application Development using GenAI, wherein I will use OpenAI's GPT series of models and Google's Gemini.
Who this course is for
■ Application Developers
■ Solution Architects
■ Data Architects
■ Data Engineers
■ People who want to learn GenAI Development and Core Concepts
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