I’m sharing a comprehensive document that delves into the core of Data Engineering, Data Science, and Business Analysis. Whether you’re starting your career or looking to pivot into the data realm, this resource is tailored for you. 🔍 Inside, you’ll
Author:
I am sharing a series of documents that will help to understand Transformer Architecture from end to end, and in easy to understand language. Transformer Architecture remains the backbone of Generative AI LLM Models. Understanding this will help you a
In the ongoing series to explain Transformer Architecture better, this document is focused on Decoder. The Transformer decoder is a neural network component designed to generate output sequences based on encoded input representations. It employs self-attention mechanisms to understand the
Sometimes when we browse to find relevant videos, we have to spend good amount of time to see through the videos and find relevant content. In case we have a summary available for the video, a quick walkthrough can save our
In continuation in the document series of “Transformers Architecture”. Today I will be covering Encoders.Ever felt lost trying to understand Encoders in Transformer Architectures? You’re not alone!This comprehensive guide will be your one-stop shop to mastering this crucial NLP concept. Here’s
Unveiling Generative AI’s Power in Presentation Summaries with Microsoft PowerPoint Copilot Gone are the days of toiling over presentation summaries! Today in this document, I’m diving into the exciting world of Generative AI and its game-changing application in Microsoft PowerPoint Copilot.
Exploring Generative AI in Streamlining PowerPoint Presentation Creation: PowerPoint presentations are used across industries and across profession. With the recent development in AI and Gen AI field, creating PowerPoint presentation can be very easy exercise. In this document I will
Continuing our deep dive into Transformers, today we explore Multi-Head Attention, a crucial concept behind their success! Previously, we covered tokens, word embeddings, and self-attention. Now, we’ll focus on Multi-Head Attention: · From Self-Attention to Multi-Head Attention: We’ll bridge the gap
In the series – ‘Key Concepts for Transformers,’ I have covered Tokens, Tokenization, and Word Embeddings, Self Attention Mechanism so far. In this post, I will explain the maths behind the ‘Self-Attention Mechanism.’. In this post we will cover the
In the last two posts of the series ‘Key Concepts for Transformers,’ I covered Tokens, Tokenization, and Word Embeddings. In this post, I will explain the concept of ‘Self-Attention Mechanism.’ This is a crucial concept and a powerful tool for