Sandeep Sharma’s Post

Recently, I was asked about Transformers during a job interview, and I couldn't answer the question. So, I decided to dive deep into the subject and write this post to share what I've learned. Transformers are a groundbreaking architecture introduced in the paper “Attention is All You Need” by Google researchers in 2017. The key innovation here is the self-attention mechanism, which allows the model to focus on different parts of a sequence when processing language. Unlike previous models (RNNs, LSTMs), Transformers don’t require sequential data processing, making them faster and more efficient for large datasets. Here's a breakdown of what makes Transformers so powerful: - Self-Attention: Each word in a sentence can attend to all other words, allowing the model to capture dependencies regardless of distance. - Parallelization: Since Transformers don't rely on sequential processing, they can process data in parallel, leading to faster training times. - Scalability: This architecture forms the backbone of modern Large Language Models (LLMs) like GPT and BERT, which have scaled to billions of parameters. The original Google paper revolutionized natural language processing and led to advancements that power many of today’s AI applications. For anyone diving into NLP or LLMs, understanding Transformers is crucial. Here's a link to the paper that started it all: https://lnkd.in/da5v48Sd #MachineLearning #NLP #Transformers

Sometimes it’s useful to go back and read papers from the past. Some things become more clear. “Attention is All You Need” was first published in 2017 by the group of researches at Google, made a big difference and allowed to create many of generative AI products we’re using today. 📝 Key Concepts: - Self-Attention: Each word in a sentence looks at every other word, creating dynamic connections. This enables the model to weight the importance of each word in understanding context. - Query, Key, Value: Transforming input tokens into Q, K, and V vectors allows the model to compute attention scores and weight the values accordingly. - Scaled Dot-Product Attention: Scores are scaled to manage the magnitude, followed by a softmax to create a probability distribution. - Multi-Head Attention: Multiple attention heads operate in parallel, capturing diverse aspects of the input sequence. - Positional Encoding: Adds sequential information to embeddings, compensating for the lack of inherent order in transformers. ⭐️ Why It’s Revolutionary: - Parallelisation: Unlike recurrent neural nets (RNNs), transformers process entire sequences simultaneously, dramatically improving efficiency. - Long-Range Dependencies: Direct connections between any two tokens enable the model to understand context over long distances, which RNNs often struggle with. - Scalability: Transformers scale effectively with increased data and computational power, achieving superior performance on various tasks. - Versatility: From natural language processing to computer vision, the attention mechanism's flexibility has broadened the horizon for AI applications. #ai #ml #genai #nlp #transformer #google #learning https://lnkd.in/eCq3ViQq

Day 15: Positional Encoding – Giving Transformers a Sense of Order Welcome to Day 15 of our 30-day Generative AI Interview Preparation Series! Today, we’ll dive into Positional Encoding, a vital mechanism that helps Transformers understand the order of input sequences, which is crucial for tasks like language modeling and translation. 1️⃣ Why Do We Need Positional Encoding? Unlike RNNs or LSTMs, Transformers process all input tokens simultaneously, meaning they lack a built-in sense of order. Positional Encoding injects information about the position of tokens in a sequence, ensuring that the model can distinguish between "The cat sat on the mat" and "The mat sat on the cat." 2️⃣ How Positional Encoding Works Positional Encoding is added to the input embeddings, providing unique positional information for each token. It typically uses sine and cosine functions to encode positions. Formulas: pos = position of the token in the sequence. i = dimension index. d = dimensionality of the embedding. 3️⃣ Key Features Unique Encoding: Each position gets a distinct encoding, differentiating every token's position in the sequence. Periodic Structure: The use of sine and cosine ensures that the encoding can handle long sequences by providing periodic patterns. 4️⃣ Why It’s Important Without Positional Encoding, Transformers would treat all tokens as if they were unordered, making them ineffective for sequential tasks. This encoding helps the model grasp temporal relationships in text or other data. 5️⃣ Common Interview Questions Why do Transformers use sine and cosine functions for Positional Encoding? Can you explain how positional information impacts attention scores? Recommended Resources 📖 Illustrated Positional Encoding 📚 Attention is All You Need (Section on Positional Encoding) https://lnkd.in/gDMdQFUq Follow Arshad Aafaq.D for more insights, and stay tuned for tomorrow’s post on Feed-Forward Networks in Transformers! 🌟 #GenerativeAI #AIInterviewPrep #Transformers #DeepLearning #NLP #PositionalEncoding #MachineLearning #TechCareers #AIInnovation

Uncovering the Power of Transformers in LLMs Today, I dived into transformers—not the electrical kind (though many Nigerians might think so! 😂)—but the transformers powering Large Language Models (LLMs). On Day 4 of my journey to building LLMs from scratch, I explored the basics of transformer architecture. I learned that many advanced LLMs are built on this architecture, a deep neural network introduced in the groundbreaking 2017 research paper "Attention Is All You Need" by Vaswani et al. A bit of back story: transformers were initially developed for language translation, specifically to handle translations from English to languages like German and French. Attention Is All You Need introduced the “attention mechanism,” a feature allowing transformers to capture context in text by focusing on relevant sections without requiring sequential processing. This approach gave transformers a major advantage over older models like RNNs(Recurrent Neural Networks), which relied heavily on step-by-step processing. The attention mechanism transformed LLMs, making them adaptable for a wide range of applications beyond translation. This flexibility led to the use of transformers in models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformers). BERT is ideal for predicting missing words in text, while GPT excels in generating new text, making these models valuable tools for text generation, summarization, question answering, and more. Exploring transformers has opened my eyes to how this architecture is reshaping AI and language processing. There are more exciting things I'd like to share with you, so connect with me if you haven't and let's learn together. #LLMs #AI #datascience #machinelearning #learning

"Attention Is All You Need" introduces the Transformer model, a revolutionary approach in sequence processing that relies entirely on self-attention mechanisms, offering unprecedented efficiency and performance in natural language processing tasks. - 🧠 Self-Attention: Replaces traditional recurrent networks, focusing on relevant input elements, leading to a better understanding of sequences. - ⚡ Parallelization: Significantly reduces training time by enabling simultaneous processing of data. - 🌍 Benchmark Results: Achieves state-of-the-art performance in translation tasks, surpassing previous models with superior BLEU scores. #AI #MachineLearning #DeepLearning - 🔍 Model Simplicity: Simplifies architecture by removing the need for convolutional and recurrent layers. - 🚀 Scalability: Easily scales to handle large datasets due to its parallel processing capabilities. - 🌟 Versatility: Applicable across various tasks like translation, summarization, and text generation. - 🔗 Interconnectedness: Self-attention allows the model to consider the entire sequence, improving contextual understanding. - 📈 Performance Gains: Demonstrates remarkable gains in speed and accuracy, making it a foundational model in modern AI research. https://lnkd.in/gpukDe9F

Want to get started in Generative AI and enjoy reading technical papers. Then here are the list of some important research articles leading to the development of the SOTA models: 1. Attention is all you need https://lnkd.in/efgn2yKY This key paper introduces Transformer architecture which are the foundation of GPTs and other LLMs 2. BERT (Bidirectional Encoder Representations from Transformers) https://lnkd.in/e-tHjguX This paper popularized the applications of Transformer models in NLP 3. T5 (Text-to-Text Transfer Transformer) https://lnkd.in/eMMvAG3P This paper presented a unified approach to NLP tasks by converting all problems into a text-to-text format 4. GPT-3 (Language Models are Few-Shot Learners) https://lnkd.in/e7tqyEiT This paper introduced GPT-3 which can perform wide variety of tasks with minimal fine tuning 5. LoRA: Low-Rank Adaptation of Large Language Models https://lnkd.in/eg4Z25qS This paper showed how to efficiently perform fine-tuning of LLMs 6. Llama 2: Open Foundation and Fine-Tuned Chat Models https://lnkd.in/ecWiiFfj This paper discusses various techniques to reduce the computational requirements of training LLMs without sacrificing performance. 7. Prompt Engineering https://lnkd.in/ePexz7Y9 This paper provides a structured approach to prompt engineering for enhancing interactions with ChatGPT and LLMs. Let me know which paper you're reading this week As always happy learning!

Paper Spotlight: "Attention Is All You Need" by Ashish Vaswani et al. (2017) - Introduces the Transformer architecture that has revolutionized how we approach sequence modelling by relying solely on attention mechanisms, moving beyond traditional models like RNNs and CNNs. Key Concepts: - Self-Attention Mechanism: The Transformer processes input sequences by weighing the relevance of different parts without relying on recurrent connections. - Encoder-Decoder Structure: Similar to earlier sequence-to-sequence models, but built entirely with self-attention layers. - Parallelism: Its highly parallelizable architecture enables faster training compared to RNN-based models. Why It’s a Game-Changer: - State-of-the-Art Performance across tasks like translation, summarization, and question answering. - Efficiency through faster training and inference times. - Foundation for Modern NLP: Transformers paved the way for large models like GPT-3 and BERT, which have reshaped natural language processing. Key Innovations: - Self-Attention: Allows the model to focus on relevant parts of the input sequence. - Positional Encoding: Incorporates positional information without needing recurrent layers. - Multi-Head Attention: Enables simultaneous learning of multiple representations of the input. Impact: - NLP Revolution: Transformer’s architecture has driven breakthroughs in various NLP tasks. - Foundation for Large Models: It’s the backbone of powerful models that now generate human-like text. In short, the paper introduced the Transformer, which has since become the foundation for many cutting-edge models, significantly advancing the fields of NLP and deep learning. Paper link : https://lnkd.in/g4MJeFzi #MachineLearning #DeepLearning #MLjourney #DataScience #AI

🚀 𝐓𝐨𝐝𝐚𝐲 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: 𝐒𝐞𝐥𝐟-𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬 🚀 Hi, I’m 𝗦𝗶𝗺𝗼𝗻 (𝗔𝘆𝗼) 𝗔𝘆𝗮𝗻𝗸𝗼𝗷𝗼, a recent graduate on my journey into the world of 𝗠𝗟𝗢𝗽𝘀, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, and 𝗔𝗜. Today, I learned about 𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 from the paper "𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑰𝒔 𝑨𝒍𝒍 𝒀𝒐𝒖 𝑵𝒆𝒆𝒅." You can check it out here: https://lnkd.in/eb3cxwyk Here’s the breakdown of some of what I have learned: 1️⃣ 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 Self-attention links all words in a sentence simultaneously, based on 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, not just their positions. 2️⃣  𝗖𝗮𝗽𝘁𝘂𝗿𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 It calculates relationships between words, allowing it to capture 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀 regardless of distance from each other. 3️⃣ 𝗤𝘂𝗲𝗿𝘆, 𝗞𝗲𝘆, 𝗩𝗮𝗹𝘂𝗲 Each word has a 𝗾𝘂𝗲𝗿𝘆, 𝗸𝗲𝘆, and 𝘃𝗮𝗹𝘂𝗲 to retrieve the most relevant information. 4️⃣ 𝗪𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 More  𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗿𝗲𝗹𝗮𝘁𝗲𝗱 words get more attention, even if they are far apart in the sentence. I am excited to see how my journey into the world of 𝗠𝗟𝗢𝗽𝘀, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, and 𝗔𝗜 goes! #TechJourney #MachineLearning #NLP #AttentionMechanisms #MLOps #AI

A Deep Dive into the Transformer Architecture https://lnkd.in/gUAd_U3W The world of Artificial Intelligence (AI) has seen remarkable advancements in recent years, particularly in the field of natural language processing (NLP). As someone who has been closely following these developments, I have been fascinated by the emergence of Large Language Models (LLMs) and their ability to understand and generate human-like text. At the heart of these advanced AI models lies the Transformer architecture, a groundbreaking innovation that has transformed the way machines process and interpret language. In this article, I will explore the inner workings of the Transformer architecture and shed light on how it powers cutting-edge language AI. Whether you're a tech enthusiast, a business professional, or simply curious about the future of AI, understanding the Transformer is key to grasping the potential of LLMs and their impact on various industries. Notable LLMs Based on the Transformer Architecture GPT, BERT, PALM and many more The Transformer: The Transformer architecture was introduced in a 2017 research paper titled "Attention Is All You Need" by Vaswani et al. This neural network design has since become the foundation of state-of-the-art LLMs, enabling them to process input sequences (such as sentences or paragraphs) and generate output sequences with unparalleled accuracy and fluency. At its core, the Transformer consists of two main components: an encoder and a decoder. The encoder takes the input sequence and processes it to capture the contextual information and relationships between words. The decoder then uses this information to generate the output sequence, one word at a time. What sets the Transformer apart from previous architectures is its self-attention mechanism, which allows the model to weigh the importance of different parts of the input when processing each word. This mechanism helps the model understand which words are most relevant to each other, regardless of their position in the sequence. The Business Impact of LLMs As LLMs continue to advance, powered by the Transformer architecture, they are increasingly being applied to solve real-world business challenges. From customer service and content creation to sentiment analysis and language translation, LLMs are transforming the way organizations interact with their customers and operate in the global marketplace. One of the most prominent applications of LLMs is in the development of sophisticated chatbots and virtual assistants. Another area where LLMs are making a significant impact is sentiment analysis. By analyzing large volumes of text data, such as customer reviews and social media comments, LLMs can help businesses gauge public opinion and sentiment towards their brand or products. HealthCare: Analyzing medical data, aiding drug discovery, and supporting clinical decisions by identifying patterns and generating insights.

🌟 Understanding the Foundations of Generative AI 🌟 If you're diving into the world of Generative AI, it's crucial to grasp the basics—and what better way than to revisit "Attention is All You Need" (https://lnkd.in/gkUStRx2)? This paper introduced the Transformer model, the bedrock of modern generative AI systems like GPT and BERT. 💡 Key Insight: Mastering concepts like self-attention and transformer architecture is essential to understanding how today’s generative models operate and innovate. Start with the basics: https://lnkd.in/gkUStRx2 #AI #GenerativeAI #NLP #MachineLearning #AIResearch