The Rise of Transformers in Vision and Multimodal Models – Hugging Face – day 72

The Rise of Transformers in Vision and Multimodal Models In this first part of our blog series, we’ll explore how transformers, originally created for Natural Language Processing (NLP), have expanded into Computer Vision (CV) and even multimodal tasks, handling text, images, and video in a unified way. This will set the stage for Part 2, where we will dive into using Hugging Face and code examples for practical implementations. 1. The Journey of Transformers from NLP to Vision The introduction of transformers in 2017 revolutionized NLP, but researchers soon realized their potential for tasks beyond just text. Originally used alongside...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

Mastering NLP: Unlocking the Math Behind It for Breakthrough Insights with a scientific paper study – day 71

What is NLP and the Math Behind It? Understanding Transformers and Deep Learning in NLP Introduction to NLP Natural Language Processing (NLP) is a crucial subfield of artificial intelligence (AI) that focuses on enabling machines to process and understand human language. Whether it’s machine translation, chatbots, or text analysis, NLP helps bridge the gap between human communication and machine understanding. But what’s behind NLP’s ability to understand and generate language? Underneath it all lies sophisticated mathematics and cutting-edge models like deep learning and transformers. This post will delve into the fundamentals of NLP, the mathematical principles that power it, and...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
close up of a smartphone

How ChatGPT Work Step by Step – day 70

Understanding How ChatGPT Processes Input: A Step-by-Step Guide Introduction ChatGPT is a language model based on the Transformer architecture. It generates responses by processing input text through several neural network layers. By understanding each step, we can appreciate how ChatGPT generates coherent and contextually appropriate replies. Additionally, ChatGPT follows a decoder-only approach (as in the GPT family of models). This means it uses a single stack of Transformer layers to handle both the input context and the generation of output tokens, rather than having separate encoder and decoder components. Step 1: Input Tokenization What Happens? The input text is broken...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
chat gpt page on screen

Can ChatGPT Truly Understand What We’re Saying? A Powerful Comparison with BERT” – Day 69

Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text Understanding ChatGPT and BERT: A Comprehensive Analysis by...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

Brief OverView of How ChatGPT Works? – Day 68

Understanding How ChatGPT Works: A Step-by-Step Guide ChatGPT, developed by OpenAI, is a sophisticated language model capable of generating human-like responses to various queries. Understanding its architecture and functionality provides insight into how it processes and generates text. 1. Input Processing: Tokenization and Embedding When ChatGPT receives a sentence, it first performs tokenization, breaking the input into individual units called tokens. These tokens can be words or subwords. Each token is then converted into a numerical vector through a process called embedding, which captures semantic information in a high-dimensional space. Example: For the input: “Write a strategy for treating otitis...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

Do you want to read a summery of what is BERT in 2 min read? (Bidirectional Encoder Representations from Transformers) – day 67

Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text   Understanding BERT: How It Works and Why...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

Transformers in Deep Learning: Breakthroughs from ChatGPT to DeepSeek – Day 66

Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text   Table 1: Comparison of Transformers, RNNs, and...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

The Transformer Model Revolution from GPT to DeepSeek & goes on How They’re Radically Changing the Future of AI – Day 65

Exploring the Rise of Transformers and Their Impact on AI: A Deep Dive Introduction: The Revolution of Transformer Models The year 2018 marked a significant turning point in the field of Natural Language Processing (NLP), often referred to as the “ImageNet moment for NLP.” Since then, transformers have become the dominant architecture for various NLP tasks, largely due to their ability to process large amounts of data with astonishing efficiency. This blog post will take you through the history, evolution, and applications of transformer models, including breakthroughs like GPT, BERT, DALL·E, CLIP, Vision Transformers (ViTs), DeepSeek and more. We’ll explore...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

Why transformers are better for NLP ? Let’s see the math behind it – Day 64

Understanding RNNs & Transformers in Detail: Predicting the Next Letter in a Sequence We have been focusing on NLP on today article and our other two articles of  Natural Language Processing (NLP) -RNN – Day 63 &  The Revolution of Transformer Models – day 65. In this article explanation, we’ll delve deeply into how Recurrent Neural Networks (RNNs) and Transformers work, especially in the context of predicting the next letter “D” in the sequence “A B C”. We’ll walk through every step, including actual numerical calculations for a simple example, to make the concepts clear. We’ll also explain why Transformers...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here

Natural Language Processing (NLP) and RNN – day 63

Understanding RNNs, NLP, and the Latest Deep Learning Trends in 2024-2025 Introduction to Natural Language Processing (NLP)   Natural Language Processing (NLP) stands at the forefront of artificial intelligence, empowering machines to comprehend and generate human language. The advent of deep learning and large language models (LLMs) such as GPT and BERT has revolutionized NLP, leading to significant advancements across various sectors. In industries like customer service and healthcare, NLP enhances chatbots and enables efficient multilingual processing, improving communication and accessibility. The integration of Recurrent Neural Networks (RNNs) with attention mechanisms has paved the way for sophisticated models like Transformers,...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here