Category Archives: Machine Learning
Text Clustering Python Examples: Steps, Algorithms
Text clustering has swiftly emerged as a cornerstone in data-driven decision-making across industries. But what exactly is text clustering, and how can it transform the way businesses operate? How does it convert unstructured text into actionable insights? What are the core steps involved in text clustering, and how are they interlinked? What algorithms are pivotal in implementing text clustering effectively? In this blog, we will unravel these questions, diving deep into the systematic steps of text clustering, its underlying algorithms, and real-world examples that bring this technique to life. Whether you’re a product manager seeking to leverage data analytics or a data scientist curious to learn key steps of text …
Topic Modeling LDA Python Example
Are you overwhelmed by the endless streams of text data and looking for a way to unearth the hidden themes that lie within? Have you ever wondered how platforms like Google News manage to group similar articles together, or how businesses extract insights from vast volumes of customer reviews? The answer to these questions might be simpler than you think, and it’s rooted in the world of Topic Modeling. Introducing Latent Dirichlet Allocation (LDA) – a powerful algorithm that offers a solution to the puzzle of understanding large text corpora. LDA is not just a buzzword in the data science community; it’s a mathematical tool that has found applications in …
Encoder Only Transformer Models Quiz / Q&A
Are you intrigued by the revolutionary world of transformer architectures? Have you ever wondered how encoder-only transformer models like BERT, ELECTRA, or DeBERTa have reshaped the landscape of Natural Language Processing (NLP)? The rapid advancement of machine learning has led to the creation of numerous transformer architectures, each with unique features, applications, and underlying mechanics. Whether you’re a data scientist, machine learning engineer, generative AI enthusiast, or a student eager to deepen your understanding, this quiz offers an engaging and informative way to assess your knowledge and sharpen your skills. It would also help you prepare for your interviews on this topic. Encoder-only transformer models have become a cornerstone in …
Hypothesis Testing Steps & Examples
Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events in order to establish new knowledge. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, how to formulate them and how to use them in …
Huggingface Arxiv Dataset: Python Example
Working with large and specific datasets is a common requirement in the field of natural language processing (NLP) and machine learning. The Arxiv dataset, containing metadata such as titles, abstracts, years, and categories of research papers, is an invaluable resource for researchers and data scientists. How can we easily load this dataset and extract the required information? In this blog post, we will explore a Python example using the Hugging Face library to load the Arxiv dataset and extract specific metadata. Python Code for Loading Huggingface Arxiv Dataset The following are the steps to load Hugging face Arxiv dataset using python code: Real-World Application Use Cases: Analyzing Research Papers Imagine …
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 …
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 …
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. …
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 …
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 …
I found it very helpful. However the differences are not too understandable for me