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Posts Related to Machine Learning

Transformers: The Game-Changing Breakthrough in Machine Learning – Why They Outshine RNNs and LSTMs

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Transformers have revolutionized the field of machine learning, especially in Natural Language Processing (NLP) and Computer Vision. They have become the foundation of many state-of-the-art models, such as BERT, GPT, and Vision Transformers. In this post, we will dive into the history, motivation, and reasons behind the development of Transformers, the challenges with previous models, how Transformers solve these issues, and their key applications.

Published on: September 01, 2024

Inside the Encoder-Decoder Architecture of Transformers

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In the previous post, we introduced the significance of Transformers and how they overcame the limitations of traditional sequence models like RNNs and LSTMs. Now, let’s delve into the core architecture of the Transformer model and explore how its components work together to achieve state-of-the-art performance in various tasks.

Published on: September 02, 2024

Attention Mechanism in Transformers: The Real Hero Behind the Magic!

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My friend! Today, we’re diving deep into the heart of what makes Transformers so powerful — the Attention Mechanism. Imagine a classroom where a teacher is trying to explain a concept. Now, every student (or token, in our case) is trying to figure out which other students to pay attention to! That’s what we call Self-Attention. And let me tell you, the magic of Transformers lies right here. Let’s break it down, step by step, in a way that feels like chai on a rainy day.

Published on: September 04, 2024

Understanding Positional Encoding in Transformers

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Ah, welcome to the frontier of artificial intelligence, where things aren't always what they seem. Today, we'll dive into Positional Encoding, a core concept that allows the Transformer model to understand the order of words in a sequence, even though it doesn’t have the recurrent structure like RNNs. Now, imagine you're reading a book. The order of the words matters, doesn’t it? You wouldn't want the sentence "The cat sat on the mat" to be rearranged to "On the cat mat sat the." The meaning is entirely lost without sequence information! Transformers face the same problem —without Positional Encoding, the model wouldn't know the order of tokens in a sentence. So, let’s unravel this enigma step by step.

Published on: September 06, 2024

Exploring Advanced Transformer Variants: Vision Transformers, GPT-3, and the Latest Models

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Transformers have had a massive impact on artificial intelligence, especially in natural language processing (NLP). The innovation of transformer architecture has not only transformed NLP but has also extended to various other fields, including computer vision and multimodal tasks. In this article, we will explore the advanced transformer variants and their real-world applications. We will focus on Vision Transformers (ViT), the revolutionary GPT-3, and the most recent models like Mistral and LLaMA.

Published on: September 14, 2024

Why is Everyone Obsessed with LLMs, Mistral, GPT4, Langchain, LLama etc?

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The fascination with Large Language Models (LLMs) has surged dramatically over the past few years. LLMs like GPT-4, BERT, and T5 have transformed how we think about artificial intelligence, communication, and problem-solving in various domains, including natural language processing (NLP), search engines, customer service, and even creative writing. But why has this obsession taken hold, and should these models replace more traditional or smaller models based on Convolutional Neural Networks (CNNs) or smaller transformer architectures? This article delves into why LLMs have garnered so much attention, explores the potential trade-offs between LLMs and smaller models, discusses the economic and computational costs of using LLMs in production, and examines whether everyone should migrate to using LLMs or not.

Published on: August 29, 2024

LLM Craze: Profit or Loss? Are Giants Like Google, OpenAI, Meta Just Burning Money?

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In recent years, Large Language Models (LLMs) have taken the tech world by storm. Companies like Google, OpenAI, Meta, and Mistral have poured billions into the research, development, and deployment of these sophisticated models, making them the heart of new-age Artificial Intelligence (AI) services. From chatbots like ChatGPT to language translation, content creation, and even assisting in software development—LLMs are everywhere! But behind this glamour lies a big question: Are these companies really making profits, or are they quietly bleeding money due to the huge computational costs involved in training and hosting these models? Is it truly a money-making venture, or is the LLM hype masking financial losses? Let’s dive deep and see if LLMs are truly profitable or just a technological buzz.

Published on: August 25, 2024

Exploring the Power of Multi-Modal Deep Learning: Bridging the Gap Between Data Types

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In the realm of artificial intelligence, the ability to process and understand diverse forms of data is transforming how machines interact with the world. Multi-modal deep learning, which integrates various types of data—such as text, images, audio, and video—enhances the capabilities of AI systems, making them more intelligent and adaptable. This blog post dives into the architecture, applications, and significance of multi-modal deep learning, illustrating why this approach is essential in today’s data-rich landscape.

Published on: September 23, 2024

Unlocking Document Understanding: An In-Depth Look at LayoutLMv3

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Introduction In the age of digitization, understanding and extracting information from documents is becoming increasingly critical. Whether it's invoices, contracts, or forms, the ability to accurately interpret complex layouts and textual information is vital for numerous applications. Enter LayoutLMv3, a cutting-edge multi-modal model specifically designed for document understanding tasks. This article delves into the architecture of LayoutLMv3, its advantages over earlier versions, and the various use cases it enables.

Published on: September 15, 2024

How To Create Synthetic Dataset for Donut OCR for your custom language?

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In this tutorial we will learn to create a synthetic dataset for training Donut (Vision Transformer and Decoder) on custom languages (Right to Left ) such as Arabic, Hindi, Urdu, Hebrew etc This tutorial is designed for Machine Learning Engineers who want to create synthetic datasets for RTL (Right-to-Left) languages like Arabic, Urdu, Persian, Hebrew, and Hindi using SynthDoG. We'll walk you through generating dataset images using Urdu as an example. These datasets can be tailored for other RTL languages by modifying configurations appropriately.

Published on: October 26, 2024

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