Category: Generative AI

One-Hot Encoding, Dot Product and Matrices multiplication: The Basics of Transformers

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Introduction In the world of natural language processing (NLP), everything begins with words. However, computers don’t understand words directly – they need numbers. Our first task is to convert words into numerical representations so that we can perform mathematical operations on them. This is especially important when building systems like voice-activated assistants, where we need to transform sequences of sounds into sequences of words. To achieve this, we start by defining a Vocabulary, which is the set of symbols (or words) we’ll be working with. For simplicity, let’s assume we’re working with English, which has tens of thousands of words,…

Fine-Tuning Large Language Models: A Technical Overview

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The prowess of Large Language Models (LLMs) like LLAMA 7B and ChatGPT in mimicking human-like text has been a game-changer in AI. Yet, their broad capabilities often fall short in specialized tasks. Fine-tuning is the bridge to this gap, applying principles from transfer learning to tailor LLMs to specific domains. Why Fine-Tuning? Drawing from Transfer Learning: Consider a Convolutional Neural Network trained in general image recognition. While adept across a range of objects, its specificity falls short with dog breeds. Here, transfer learning refines the model by fine-tuning it with a dataset focused on dog breeds, enhancing its specificity. Similarly,…

Foundation of AI Brilliance: Unpacking Pre-Training of Large Language Models

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In the mesmerizing realm of Artificial Intelligence, the journey of a Large Language Model (LLM) from a nascent stage to a wise oracle capable of understanding and generating human-like text is nothing short of a marvel. At the heart of this journey lies the process of Pre-Training—a phase of paramount importance that shapes the core intelligence of LLMs like ChatGPT. This article aims to demystify Pre-Training, offering insights that cater to both AI novices and data science veterans, while also highlighting the broader implications, including environmental considerations. Understanding Pre-Training: Pre-Training is the initial learning phase where a model, such as…

Layers of Generative AI: Pre-Training, Fine-Tuning, and Retrieval Augmented Generation

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In the rapidly evolving landscape of artificial intelligence, Generative AI stands out, driving innovations across various sectors. This transformative technology is built on foundational processes like Pre-Training, Fine-Tuning, and Retrieval Augmented Generation (RAG). Today, let’s explore these processes not just for their technical intricacies but also through the lens of cost and time investment, key factors that shape the deployment and scalability of these AI solutions. Pre-Training: The Costly Foundation Pre-Training is where a model learns from a vast array of data, gaining a broad understanding of language, concepts, or images. This stage is very similar to setting up the…
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