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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 Convolutional Neural Networks (CNNs), transformers were able to handle image captioning tasks by replacing older architectures like Recurrent Neural Networks (RNNs). How Transformers Replace RNNs Transformers replaced RNNs due to their ability to capture long-term dependencies and work in parallel rather than sequentially, like RNNs. This made transformers faster and more efficient, especially for image-based tasks where multiple features needed to be processed simultaneously. 2. The Emergence of Vision Transformers (ViT) In 2020, researchers at Google proposed a completely transformer-based model for vision tasks, named the Vision Transformer (ViT). ViT treats an image in a way similar to text data—by...

Understanding How ChatGPT Processes Input: A Step-by-Step Guide 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 down into smaller units called tokens. ChatGPT uses a tokenizer based on Byte Pair Encoding (BPE). Neural Network Involvement: No — Tokenization is a preprocessing step, not part of the neural network. Example: Input Text: “Hi” Tokenization Process: Text Token ID “Hi” 2 Figure 1: Tokenization Input Text: “Hi” ↓ Tokenization ↓ Token IDs: [2] Step 2: Token Embedding What Happens? Each token ID is mapped to a token embedding vector using an embedding matrix. The embedding represents the semantic meaning of the token. Neural Network Involvement: Yes — This is part...

  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 in a young adult,” the tokenization might yield tokens like “Write,” “a,” “strategy,” etc. Each of these tokens is then mapped to a corresponding vector in the embedding space. 2. Decoder-Only Architecture: Contextual Understanding and Response Generation Unlike traditional transformer models that utilize an encoder-decoder architecture, ChatGPT employs a decoder-only structure. This design allows the model to handle both understanding the input and generating responses within a single framework. The model uses self-attention mechanisms to capture relationships between tokens, enabling it to understand context and generate coherent outputs. Key Points: Self-Attention: Allows the model to weigh the importance of...

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 both the theoretical concepts behind these models and their practical implementations using Hugging Face’s libraries. The Rise of Transformer Models in NLP In 2018, the introduction of the GPT (Generative Pre-trained Transformer) paper by Alec Radford and OpenAI was a game-changer for NLP. Unlike earlier methods like ELMo and ULMFiT, GPT used a transformer-based architecture for unsupervised pretraining, proving its effectiveness in learning from large datasets. The architecture involved a stack of 12 transformer modules, leveraging masked multi-head attention layers, which allowed it to process language efficiently. This model was revolutionary because it could pretrain on a vast corpus of...

  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 are considered as neural networks and how they fit into the broader context of deep learning.  Recurrent Neural Networks (RNNs) Introduction to RNNs RNNs are a type of neural network designed to process sequential data by maintaining a hidden state that captures information about previous inputs. This makes them suitable for tasks like language modeling, where the context provided by earlier letters influences the prediction of the next letter. Problem Statement Lets say, Given the sequence of “A B C”, we want the RNN to predict the next letter, which is “D”. Input Representation We need to represent each...

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, which have become instrumental in shaping the future of NLP. Transformers, introduced in 2017, utilize attention mechanisms to process language more effectively than previous models. Their ability to handle complex language tasks has led to the development of advanced LLMs, further propelling NLP innovations. Wikipedia As NLP continues to evolve, the focus is on creating more efficient models capable of understanding and generating human language with greater accuracy. This progress holds promise for more natural and effective interactions between humans and machines, transforming various aspects of daily life. NLP has achieved deeper contextual understanding, enabling models to grasp nuances such as...