Category Archives: Machine Learning
Encoder-only Transformer Models: Examples
How can machines accurately classify text into categories? What enables them to recognize specific entities like names, locations, or dates within a sea of words? How is it possible for a computer to comprehend and respond to complex human questions? These remarkable capabilities are now a reality, thanks to encoder-only transformer architectures like BERT. From text classification and Named Entity Recognition (NER) to question answering and more, these models have revolutionized the way we interact with and process language. In the realm of AI and machine learning, encoder-only transformer models like BERT, DistilBERT, RoBERTa, and others have emerged as game-changing innovations. These models not only facilitate a deeper understanding of …
LLMs & Semantic Search Course by Andrew NG, Cohere & Partners
Andrew Ng, a renowned name in the world of deep learning and AI, has joined forces with Cohere, a pioneer in natural language processing technologies. Alongside him are Jay Alammar, a well-known educator and visualizer of machine learning concepts, and Serrano Academy, an esteemed institution dedicated to AI research and education. Together, they have launched an insightful course titled “Large Language Models with Semantic Search.” This collaboration represents a fusion of expertise aimed at addressing the growing needs of semantic search in various applications. In an era where keyword search has dominated the search landscape, the need for more sophisticated, content-aware search capabilities is becoming increasingly evident. Content-rich platforms like …
Transformer Architecture Types: Explained with Examples
Are you fascinated by the power of deep learning models that can translate languages, generate creative writing, and even answer complex questions? Ever wondered how a machine can understand and process human language with such finesse? At the heart of these remarkable achievements lies a machine learning model architecture that has revolutionized the field of Natural Language Processing (NLP) – the Transformer architecture, a deep learning architecture. But what makes Transformer models so special? How do they manage to encode the subtle nuances of language and context? Can we understand the complex mathematical machinery that operates behind the scenes? Whether you’re a seasoned data scientist, an aspiring machine learning engineer, …
Quiz: BERT & GPT Transformer Models Q&A
Are you fascinated by the world of natural language processing and the cutting-edge generative AI models that have revolutionized the way machines understand human language? Two such large language models (LLMs), BERT and GPT, stand as pillars in the field, each with unique architectures and capabilities. But how well do you know these models? In this quiz blog, we will challenge your knowledge and understanding of these two groundbreaking technologies. Before you dive into the quiz, let’s explore an overview of BERT and GPT. BERT (Bidirectional Encoder Representations from Transformers) BERT is known for its bidirectional processing of text, allowing it to capture context from both sides of a word …
Transfer Learning vs Fine Tuning: Differences
Generative AI is revolutionizing various domains, from natural language processing to image recognition. Two concepts that are fundamental to these advancements are Transfer Learning and Fine Tuning. Despite their interconnected nature, they are distinct methodologies that serve unique purposes when training large language models (LLMs) to achieve different objectives. In this blog, we will explore the differences between Transfer Learning and Fine Tuning, learning about their individual characteristics and how they come into play in real-world scenarios with the help of examples. What is Transfer Learning? Transfer Learning is an AI / ML concept that refers to the utilization of a pre-trained model on a new but related task. It …
7 Free MIT AI / Machine Learning Courses: Enroll Now!
Are you eager to dive into the world of machine learning and AI but worried about the costs? Are you fascinated by how data analytics can shape the future of various industries? What if you could access top-notch education from one of the leading institutions in the world, absolutely free? In the next six months, MIT is offering seven upcoming free courses designed to equip you with the knowledge and skills in machine learning, AI, and data analytics. Whether you’re a seasoned professional looking to upskill or a beginner ready to embark on a new journey, these courses provide an incredible opportunity. In this blog, we’ll delve into the details …
Pre-training vs Fine-tuning in LLM: Examples
Are you intrigued by the inner workings of large language models (LLMs) like BERT and GPT series models? Ever wondered how these models manage to understand human language with such precision? What are the critical stages that transform them from simple neural networks into powerful tools capable of text prediction, sentiment analysis, and more? The answer lies in two vital phases: pre-training and fine-tuning. These stages not only make language models adaptable to various tasks but also bring them closer to understanding language the way humans do. In this blog, we’ll dive into the fascinating journey of pre-training and fine-tuning in LLMs, complete with real-world examples. Whether you are a …
IIT Madras Fellowship in AI for Social Good
Are you an AI researcher driven by the passion to make a positive impact on society? Do you seek to use your knowledge in machine learning and AI to contribute to real-world issues? Are you intrigued by the idea of joining a leading interdisciplinary research center for data science in India? Then here is the opportunity to discover a unique opportunity that aligns with your aspirations and expertise at the Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. Apply Now for fellowship program in AI for social good. About RBCDSAI RBCDSAI is one of India’s pre-eminent interdisciplinary research academic centers specializing in Data Science and AI. …
BERT vs GPT Models: Differences, Examples
Are you intrigued by the world of natural language processing (NLP) and the cutting-edge machine learning models that power it? Have you ever wondered what sets apart two of the most prominent models in the field, Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT)? These models have revolutionized the way machines understand and generate human language, but what exactly differentiates them? In this blog, we will delve into the core architecture, training objectives, real-world applications, examples and more. By exploring these aspects, we’ll learn about the unique strengths and use cases of both models, providing you with insights that can guide your next project or research endeavor. …
Generative AI – Concepts, Use Cases, Examples
Machine learning has rapidly evolved over the past few years, with new techniques and methods emerging regularly. One of the most exciting and promising areas in this field is Generative AI. This is also termed as generative modeling. Generative AI or generative modeling refers to the modeling algorithms / methods which results in the creation of new data samples in use cases such as text generation, text summarization, machine translation, image generation etc. This technique has gained immense popularity in recent times due to its ability to generate text, highly realistic images, videos, and music. As a data scientist, it is crucial to understand generative AI / modeling and its …
Machine Learning Projects for Final Year Students: Examples
As aspiring data scientists, computer scientists, and statisticians, the final year of your academic journey presents a perfect opportunity to showcase your skills and knowledge in practical applications. In this blog, we will explore a diverse set of exciting machine-learning projects that are well-suited for final-year students. These projects cover various domains, including education, healthcare, crime prediction, and more. We will delve into each project’s description, problem type (classification, regression, etc.), and the methods used for analysis. Whether you are seeking inspiration for your final year project or simply eager to explore the power of machine learning in real-world scenarios, this blog has something for everyone! In case you would …
Exploring Amazon Science Publications: A Quick Guide
In the ever-evolving world of technology and research, staying updated with the latest advancements is crucial. Amazon Science Publications has emerged as a treasure trove for those hungry for knowledge, offering a plethora of articles that span a wide range of topics. Whether you’re an AI / ML researcher, a student, or just a curious mind, this platform has something for everyone. Let’s delve into the vast ocean of articles available on Amazon Science Publications. Research Areas: Tags: Conferences: Journals: Date: Whether you’re looking for the latest articles from 2023 or want to revisit the gems from 2015, Amazon Science Publications has got you covered. With articles spanning from 2015 …
Retrieval Augmented Generation (RAG) & LLM: Examples
Have you ever wondered how to seamlessly integrate the vast knowledge of Large Language Models (LLMs) with the specificity of domain specific knowledge or external databases? As the world of machine learning continues to evolve, the need for more sophisticated and contextually relevant responses from models becomes paramount. For data scientists and product managers keen on deploying LLMs in production, the Retrieval Augmented Generation (RAG) pattern offers a compelling solution. In this blog, we’ll dive deep into the RAG pattern, illustrating its power and potential with practical examples. Whether you’re aiming to enhance your product’s AI capabilities or simply curious about the next big thing in machine learning, this exploration …
Greedy Search vs Beam Search Decoding: Concepts, Examples
Have you ever wondered how machine learning models transform their intricate calculations into clear, human-readable language? Or how your smartphone knows exactly what you’re going to type next before you even start typing? These everyday marvels are powered by a critical component of natural language processing (NLP) known as ‘decoding methods‘. But how do these methods work, and why are there different types? In the vast field of machine learning, a primary challenge in natural language processing tasks is converting a model’s computational output into an understandable and coherent text. Whether it’s autocompleting your sentences, translating text from one language to another, or generating a news article, these tasks involve …
NPTEL’s Machine Learning & Data Science Online Courses (Jul-Nov 2023)
In the rapidly evolving domains of Machine Learning, Data Science, and Artificial Intelligence, the quest for quality education and courses has become paramount. For those familiar with the educational landscape of India, the Indian Institutes of Technology (IITs) stand out as beacons of excellence. Established by the government of India, the IITs are autonomous public technical universities that are recognized globally for their outstanding curriculum, research, and innovation. Every year, thousands of students vie for a coveted spot in these institutions, and their alumni have made significant contributions to technology and research worldwide. NPTEL (National Programme on Technology Enhanced Learning), in collaboration with these premier IITs, has curated a range …
Vanishing Gradient Problem in Deep Learning: Examples
Ever found yourself wondering why your deep learning (deep neural network) model is simply refusing to learn? Or struggled to comprehend why your deep neural network isn’t reaching the accuracy you expected? The culprit behind these issues might very well be the infamous vanishing gradient problem, a common hurdle in the field of deep learning. Understanding and mitigating the vanishing gradient problem is a must-have skill in any data scientist‘s arsenal. This is due to the profound impact it can have on the training and performance of deep neural networks. In this blog post, we will delve into the heart of this issue, learning the calculus behind neural networks and …
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