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Category: machine-learning

  • Do You Really Need a Foundation Model?

    Do You Really Need a Foundation Model? LLM or custom model: how should you choose the right solution? The post Do You Really Need a Foundation Model? appeared first on Towards Data Science. Vincent Vandenbussche Go to original source

  • How to Ensure Reliability in LLM Applications

    How to Ensure Reliability in LLM Applications Learn how to make your LLM applications more robust The post How to Ensure Reliability in LLM Applications appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment

    From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment Using optimal transport to weight what matters most In LLM-generated responses The post From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment appeared first on Towards Data Science. Sudheer Singh Go to original source

  • Topic Model Labelling with LLMs

    Topic Model Labelling with LLMs Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The post Topic Model Labelling with LLMs appeared first on Towards Data Science. Petr Koráb Go to original source

  • There and Back Again: An AI Career Journey

    There and Back Again: An AI Career Journey A full circle moment 30 years in the making The post There and Back Again: An AI Career Journey appeared first on Towards Data Science. David Martin Go to original source

  • Are You Being Unfair to LLMs?

    Are You Being Unfair to LLMs? They may deserve better. The post Are You Being Unfair to LLMs? appeared first on Towards Data Science. Julian Mendel Go to original source

  • Reducing Time to Value for Data Science Projects: Part 3

    Reducing Time to Value for Data Science Projects: Part 3 Setting up a robust experimentation process The post Reducing Time to Value for Data Science Projects: Part 3 appeared first on Towards Data Science. Kristopher McGlinchey Go to original source

  • The Crucial Role of NUMA Awareness in High-Performance Deep Learning

    The Crucial Role of NUMA Awareness in High-Performance Deep Learning PyTorch model performance analysis and optimization — Part 10 The post The Crucial Role of NUMA Awareness in High-Performance Deep Learning appeared first on Towards Data Science. Chaim Rand Go to original source

  • Recap of all types of LLM Agents

    Recap of all types of LLM Agents Regular, ReAct, Chain-of-Thought, Reflexion, ToT, GoT, PoT The post Recap of all types of LLM Agents appeared first on Towards Data Science. Mauro Di Pietro Go to original source

  • How to Perform Effective Data Cleaning for Machine Learning

    How to Perform Effective Data Cleaning for Machine Learning Learn how you can improve your machine learning models using effective data cleaning The post How to Perform Effective Data Cleaning for Machine Learning appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Build Interactive Machine Learning Apps with Gradio

    Build Interactive Machine Learning Apps with Gradio Create a fun text-to-speech demo in minutes The post Build Interactive Machine Learning Apps with Gradio appeared first on Towards Data Science. Ehssan Khan Go to original source

  • Microsoft’s Revolutionary Diagnostic Medical AI, Explained

    Microsoft’s Revolutionary Diagnostic Medical AI, Explained Microsoft’s latest paper discusses a path to medical superintelligence. How close are we, really? The post Microsoft’s Revolutionary Diagnostic Medical AI, Explained appeared first on Towards Data Science. Ryan D’Cunha Go to original source

  • The Five-Second Fingerprint: Inside Shazam’s Instant Song ID

    The Five-Second Fingerprint: Inside Shazam’s Instant Song ID How Shazam recognizes songs in seconds The post The Five-Second Fingerprint: Inside Shazam’s Instant Song ID appeared first on Towards Data Science. Ashton Gribble Go to original source

  • Your Personal Analytics Toolbox

    Your Personal Analytics Toolbox Leveraging MCP for automating your daily routine The post Your Personal Analytics Toolbox appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Build Algorithm-Agnostic ML Pipelines in a Breeze

    Build Algorithm-Agnostic ML Pipelines in a Breeze The framework is now an open-source Python package for streamlined ML workflows The post Build Algorithm-Agnostic ML Pipelines in a Breeze appeared first on Towards Data Science. Mena Wang Go to original source

  • My Honest Advice for Aspiring Machine Learning Engineers

    My Honest Advice for Aspiring Machine Learning Engineers What it really takes to become a machine learning engineer The post My Honest Advice for Aspiring Machine Learning Engineers appeared first on Towards Data Science. Egor Howell Go to original source

  • Explainable Anomaly Detection with RuleFit: An Intuitive Guide

    Explainable Anomaly Detection with RuleFit: An Intuitive Guide Creating interpretable rules to characterize the identified anomalies The post Explainable Anomaly Detection with RuleFit: An Intuitive Guide appeared first on Towards Data Science. Shuai Guo Go to original source

  • Taking ResNet to the Next Level

    Taking ResNet to the Next Level Understanding how ResNeXt improves upon ResNet, with a comprehensive PyTorch implementation guide The post Taking ResNet to the Next Level appeared first on Towards Data Science. Muhammad Ardi Go to original source

  • STOP Building Useless ML Projects – What Actually Works

    STOP Building Useless ML Projects – What Actually Works How to find machine learning projects that will get you hired. The post STOP Building Useless ML Projects – What Actually Works appeared first on Towards Data Science. Egor Howell Go to original source

  • Revisiting Benchmarking of Tabular Reinforcement Learning Methods

    Revisiting Benchmarking of Tabular Reinforcement Learning Methods Introducing a modular framework and improving model performance. The post Revisiting Benchmarking of Tabular Reinforcement Learning Methods appeared first on Towards Data Science. Oliver S Go to original source

  • Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not!

    Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! An explanation of the causal assumption implicit in prescriptive modeling and how to satisfy it. The post Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! appeared first on Towards Data Science. Jarom Hulet Go to original source

  • Lessons Learned After 6.5 Years Of Machine Learning

    Lessons Learned After 6.5 Years Of Machine Learning Deep work, trends, data, and research The post Lessons Learned After 6.5 Years Of Machine Learning appeared first on Towards Data Science. Pascal Janetzky Go to original source

  • Become a Better Data Scientist with These Prompt Engineering Tips and Tricks

    Become a Better Data Scientist with These Prompt Engineering Tips and Tricks Part 1: prompt engineering for planning, cleaning, and EDA The post Become a Better Data Scientist with These Prompt Engineering Tips and Tricks appeared first on Towards Data Science. Sara Nobrega Go to original source

  • A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline

    A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline PyTorch model performance analysis and optimization — Part 8 The post A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline appeared first on Towards Data Science. Chaim Rand Go to original source

  • Pipelining AI/ML Training Workloads with CUDA Streams

    Pipelining AI/ML Training Workloads with CUDA Streams PyTorch Model Performance Analysis and Optimization — Part 9 The post Pipelining AI/ML Training Workloads with CUDA Streams appeared first on Towards Data Science. Chaim Rand Go to original source

  • Use OpenAI Whisper for Automated Transcriptions

    Use OpenAI Whisper for Automated Transcriptions Streamline your computer interactions using OpenAI’s Whisper model The post Use OpenAI Whisper for Automated Transcriptions appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Data Has No Moat!

    Data Has No Moat! Only if you ignore data quality The post Data Has No Moat! appeared first on Towards Data Science. Fabiana Clemente Go to original source

  • Reinforcement Learning from Human Feedback, Explained Simply

    Reinforcement Learning from Human Feedback, Explained Simply The one technique that made ChatGPT so smart The post Reinforcement Learning from Human Feedback, Explained Simply appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Programming, Not Prompting: A Hands-On Guide to DSPy

    Programming, Not Prompting: A Hands-On Guide to DSPy A practical deep dive into declarative AI programming The post Programming, Not Prompting: A Hands-On Guide to DSPy appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work

    Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work Transforming Independent Models into Collaborative Intelligence The post Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work appeared first on Towards Data Science. Eric Chung Go to original source

  • LLM-as-a-Judge: A Practical Guide

    LLM-as-a-Judge: A Practical Guide How to Scale LLM Evaluations Beyond Manual Review The post LLM-as-a-Judge: A Practical Guide appeared first on Towards Data Science. Shuai Guo Go to original source

  • What PyTorch Really Means by a Leaf Tensor and Its Grad

    What PyTorch Really Means by a Leaf Tensor and Its Grad The secret life of leaves, gradients, and the mighty requires_grad flag The post What PyTorch Really Means by a Leaf Tensor and Its Grad appeared first on Towards Data Science. Maciej J. Mikulski Go to original source

  • Computer Vision’s Annotation Bottleneck Is Finally Breaking

    Computer Vision’s Annotation Bottleneck Is Finally Breaking A Technical Deep Dive into Auto-Labeling The post Computer Vision’s Annotation Bottleneck Is Finally Breaking appeared first on Towards Data Science. TDS Brand Studio Go to original source

  • LLaVA on a Budget: Multimodal AI with Limited Resources

    LLaVA on a Budget: Multimodal AI with Limited Resources Let’s get started with multimodality The post LLaVA on a Budget: Multimodal AI with Limited Resources appeared first on Towards Data Science. Marcello Politi Go to original source

  • Grad-CAM from Scratch with PyTorch Hooks

    Grad-CAM from Scratch with PyTorch Hooks A hands-on look at an explainable AI (XAI) technique that helps reveal why a convolutional neural network (CNN) made a particular decision The post Grad-CAM from Scratch with PyTorch Hooks appeared first on Towards Data Science. Conor O’Sullivan Go to original source

  • Let’s Analyze OpenAI’s Claims About ChatGPT Energy Use

    Let’s Analyze OpenAI’s Claims About ChatGPT Energy Use ChatGPT uses an average of 0.34 Wh per query, according to a blog post by Sam Altman. Does that figure hold up? The post Let’s Analyze OpenAI’s Claims About ChatGPT Energy Use appeared first on Towards Data Science. Kasper Groes Albin Ludvigsen Go to original source

  • Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights

    Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights A detailed guide on controlling overfitting and increasing the stability of your models. The post Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights appeared first on Towards Data Science. Sourav Mohile Go to original source

  • AI Is Not a Black Box (Relatively Speaking)

    AI Is Not a Black Box (Relatively Speaking) Compared to the opacity around human intelligence, AI is more transparent in some very tangible ways. The post AI Is Not a Black Box (Relatively Speaking) appeared first on Towards Data Science. Piotr (Peter) Mardziel Go to original source

  • Connecting the Dots for Better Movie Recommendations

    Connecting the Dots for Better Movie Recommendations Connecting the Dots for Better Movie Recommendations: Lightweight graph RAG on Rotten Tomatoes movie reviews The post Connecting the Dots for Better Movie Recommendations appeared first on Towards Data Science. Brian Godsey Go to original source

  • Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox

    Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox Not just what you ask, but how you ask it. Practical techniques for prompt engineering that deliver The post Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox appeared first on Towards Data Science. Ugo Pradère…

  • Audio Spectrogram Transformers Beyond the Lab

    Audio Spectrogram Transformers Beyond the Lab A recipe for building a portable soundscape monitoring app with AudioMoth, Raspberry Pi, and a decent dose of deep learning. The post Audio Spectrogram Transformers Beyond the Lab appeared first on Towards Data Science. Maciej Adamiak Go to original source

  • Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks

    Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks A step-by-step guide to containerizing and orchestrating an ML training workflow without the Dockerfile headache, using a lightweight GPT-2 example. The post Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks appeared first on Towards Data Science. Sylvain Kalache Go to original source

  • 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC

    10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC Using GPU acceleration to speed up Bayesian Inference from months to minutes… The post 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC appeared first on Towards Data Science. Derek Tran Go to original source

  • How to Transition From Data Analyst to Data Scientist

    How to Transition From Data Analyst to Data Scientist Playbook on how data analysts can become data scientists The post How to Transition From Data Analyst to Data Scientist appeared first on Towards Data Science. Egor Howell Go to original source

  • Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling.

    Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. Learn how to move beyond prediction and actively make intervention through prescriptive modeling. This in-depth guide walks you through Bayesian approaches to system intervention, with practical examples in predictive maintenance. The post Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. appeared…

  • How I Automated My Machine Learning Workflow with Just 10 Lines of Python

    How I Automated My Machine Learning Workflow with Just 10 Lines of Python Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance. The post How I Automated My Machine Learning Workflow with Just 10 Lines of Python appeared first on Towards Data Science. Himanshu Sharma Go to original source

  • Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is

    Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is Monitoring is easy; what to monitor is not. In the field of machine learning, data drift is just noise until you know what it means. The post Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is appeared first on Towards Data Science.…

  • Pairwise Cross-Variance Classification

    Pairwise Cross-Variance Classification Multi-class zero-shot embedding classification and error checking The post Pairwise Cross-Variance Classification appeared first on Towards Data Science. Doster Esh Go to original source

  • Landing your First Machine Learning Job: Startup vs Big Tech vs Academia

    Landing your First Machine Learning Job: Startup vs Big Tech vs Academia A practical guide to landing your first Machine Learning job across startups, big tech, and academia. The post Landing your First Machine Learning Job: Startup vs Big Tech vs Academia appeared first on Towards Data Science. Piero Paialunga Go to original source

  • Decision Trees Natively Handle Categorical Data

    Decision Trees Natively Handle Categorical Data But mean target encoding is their turbocharger The post Decision Trees Natively Handle Categorical Data appeared first on Towards Data Science. Vadim Arzamasov Go to original source

  • Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning

    Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning It’s like grading papers, but your student is an LLM The post Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning appeared first on Towards Data Science. Stephanie Kirmer Go to original source

  • Your DNA Is a Machine Learning Model: It’s Already Out There

    Your DNA Is a Machine Learning Model: It’s Already Out There Even if you never sequenced your genome, predictive systems already know a lot about it. Genomic inference has become a population-scale model, and you’re probably in it. The post Your DNA Is a Machine Learning Model: It’s Already Out There appeared first on Towards…

  • Hands-On Attention Mechanism for Time Series Classification, with Python

    Hands-On Attention Mechanism for Time Series Classification, with Python This is how to use the attention mechanism in a time series classification framework The post Hands-On Attention Mechanism for Time Series Classification, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source

  • LLM Optimization: LoRA and QLoRA

    LLM Optimization: LoRA and QLoRA Scalable fine-tuning techniques for large language models The post LLM Optimization: LoRA and QLoRA appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • GAIA: The LLM Agent Benchmark Everyone’s Talking About

    GAIA: The LLM Agent Benchmark Everyone’s Talking About What practitioners need to know about this LLM agent benchmark The post GAIA: The LLM Agent Benchmark Everyone’s Talking About appeared first on Towards Data Science. Shuai Guo Go to original source

  • From Data to Stories: Code Agents for KPI Narratives

    From Data to Stories: Code Agents for KPI Narratives HuggingFace’s smolagents framework in action The post From Data to Stories: Code Agents for KPI Narratives appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • JAX: Is This Google’s NumPy killer?

    JAX: Is This Google’s NumPy killer? Auto differentiation and JIT compilation make a compelling case. The post JAX: Is This Google’s NumPy killer? appeared first on Towards Data Science. Thomas Reid Go to original source

  • Detecting Malicious URLs Using LSTM and Google’s BERT Models

    Detecting Malicious URLs Using LSTM and Google’s BERT Models A progressive approach to implementing AI-powered webpage detection applications into production The post Detecting Malicious URLs Using LSTM and Google’s BERT Models appeared first on Towards Data Science. Toluwase Babalola Go to original source

  • Tree of Thought Prompting: Teaching LLMs to Think Slowly

    Tree of Thought Prompting: Teaching LLMs to Think Slowly Playing Minesweeper with Augmented Reasoning The post Tree of Thought Prompting: Teaching LLMs to Think Slowly appeared first on Towards Data Science. Shuyang Go to original source

  • Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models

    Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models Explore how Bayesian Optimization outperforms Grid Search in efficiency and performance over binary classification tasks. The post Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models appeared first on Towards Data Science. Kuriko Iwai Go to original source

  • Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python

    Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python Inspired by AlphaGo’s Move 37 — learn how agents explore, exploit, and win The post Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python appeared first on Towards Data Science. Sarah Schürch Go to original source

  • Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives

    Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives Why splitting your objectives and your model might be the key to better performance and clearer trade-offs in deep learning. The post Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives appeared first on Towards Data…

  • Code Agents: The Future of Agentic AI

    Code Agents: The Future of Agentic AI HuggingFace smolagents framework in action The post Code Agents: The Future of Agentic AI appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • The Best AI Books & Courses for Getting a Job

    The Best AI Books & Courses for Getting a Job A comprehensive guide to the books and courses that helped me learn AI The post The Best AI Books & Courses for Getting a Job appeared first on Towards Data Science. Egor Howell Go to original source

  • Prototyping Gradient Descent in Machine Learning

    Prototyping Gradient Descent in Machine Learning Mathematical theorem and credit transaction prediction using Stochastic / Batch GD The post Prototyping Gradient Descent in Machine Learning appeared first on Towards Data Science. Kuriko Iwai Go to original source

  • Estimating Product-Level Price Elasticities Using Hierarchical Bayesian

    Estimating Product-Level Price Elasticities Using Hierarchical Bayesian Using one model to personalize ML results The post Estimating Product-Level Price Elasticities Using Hierarchical Bayesian appeared first on Towards Data Science. Derek Tran Go to original source

  • Top Machine Learning Jobs and How to Prepare For Them

    Top Machine Learning Jobs and How to Prepare For Them These days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles…

  • How to Set the Number of Trees in Random Forest

    How to Set the Number of Trees in Random Forest Scientific publication T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by determining the optimal number of trees. BMC bioinformatics, 26(1), 95. Follow this LINK to the original publication. Random Forest — A Powerful Tool for Anyone…

  • The Automation Trap: Why Low-Code AI Models Fail When You Scale

    The Automation Trap: Why Low-Code AI Models Fail When You Scale In the beginning, building Machine Learning models was a skill only data scientists with knowledge of Python could master. However, low-code AI platforms have made things much easier now. Anyone can now directly make a model, link it to data, and publish it as…

  • Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer

    Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer AlphaEvolve imagined as a genetic algorithm coupled to a large language model. Picture created by the author using various tools including Dall-E3 via ChatGPT. Large Language Models have undeniably revolutionized how many of us approach coding, but they’re often more like a super-powered…

  • Understanding Random Forest using Python (scikit-learn)

    Understanding Random Forest using Python (scikit-learn) Decision trees are a popular supervised learning algorithm with benefits that include being able to be used for both regression and classification as well as being easy to interpret. However, decision trees aren’t the most performant algorithm and are prone to overfitting due to small variations in the training…

  • How to Learn the Math Needed for Machine Learning

    How to Learn the Math Needed for Machine Learning Maths can be a scary topic for people. Many of you want to work in machine learning, but the maths skills needed may seem overwhelming. I am here to tell you that it’s nowhere as intimidating as you may think and to give you a roadmap, resources,…

  • How To Build a Benchmark for Your Models

    How To Build a Benchmark for Your Models I’ve been working as a data science consultant for the past three years, and I’ve had the opportunity to work on multiple projects across various industries. Yet, I noticed one common denominator among most of the clients I worked with: They rarely have a clear idea of…

  • Strength in Numbers: Ensembling Models with Bagging and Boosting

    Strength in Numbers: Ensembling Models with Bagging and Boosting Bagging and boosting are two powerful ensemble techniques in machine learning – they are must-knows for data scientists! After reading this article, you are going to have a solid understanding of how bagging and boosting work and when to use them. We’ll cover the following topics,…

  • Empowering LLMs to Think Deeper by Erasing Thoughts

    Empowering LLMs to Think Deeper by Erasing Thoughts Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called…

  • The Westworld Blunder

    The Westworld Blunder We’re entering an interesting moment in AI development. AI systems are getting memory, reasoning chains, self-critiques, and long-context recall. These capabilities are exactly some of the things that I’ve previously written would be prerequisites for an AI system to be conscious. Just to be clear, I don’t believe today’s AI systems are self-aware, but…

  • Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack

    Pause Your ML Pipelines for Human Review Using AWS Step Functions + Slack Have you ever wanted to pause an automated workflow to wait for a human decision? Maybe you need approval before provisioning cloud resources, promoting a machine learning model to production, or charging a customer’s credit card. In many data science and machine learning…

  • Clustering Eating Behaviors in Time: A Machine Learning Approach to Preventive Health

    Clustering Eating Behaviors in Time: A Machine Learning Approach to Preventive Health It’s well known that what we eat matters — but what if when and how often we eat matters just as much? In the midst of ongoing scientific debate around the benefits of intermittent fasting, this question becomes even more intriguing. As someone passionate about machine learning and healthy living,…

  • Model Compression: Make Your Machine Learning Models Lighter and Faster

    Model Compression: Make Your Machine Learning Models Lighter and Faster Introduction Whether you’re preparing for interviews or building Machine Learning systems at your job, model compression has become a must-have skill. In the era of LLMs, where models are getting larger and larger, the challenges around compressing these models to make them more efficient, smaller,…

  • The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help

    The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help Automl has become the gateway drug to machine learning for many organizations. It promises exactly what teams under pressure want to hear: you bring the data, and we’ll handle the modeling. There are no pipelines to manage, no hyperparameters to tune, and no…

  • A Practical Guide to BERTopic for Transformer-Based Topic Modeling

    A Practical Guide to BERTopic for Transformer-Based Topic Modeling Topic modeling has a wide range of use cases in the natural language processing (NLP) domain, such as document tagging, survey analysis, and content organization. It falls under the realm of unsupervised learning technique, making it a very cost-effective technique that reduces the resources required to…

  • From RGB to HSV — and Back Again

    From RGB to HSV — and Back Again Introduction A fundamental concept in Computer Vision is understanding how images are stored and represented. On disk, image files are encoded in various ways, from lossy, compressed JPEG files to lossless PNG files. Once you load an image into a program and decode it from the respective…

  • Retrieval Augmented Classification: Improving Text Classification with External Knowledge

    Retrieval Augmented Classification: Improving Text Classification with External Knowledge Text Classification stands as one of the most basic yet most important applications of natural language processing. It has a vital role in many real-world applications that go from filtering unwanted emails like spam, detecting product categories or classifying user intent in a chat-bot application. The…

  • Fine-Tuning vLLMs for Document Understanding

    Fine-Tuning vLLMs for Document Understanding In this article, I discuss how you can fine-tune VLMs (visual large language models, often called vLLMs) like Qwen 2.5 VL 7B. I will introduce you to a dataset of handwritten digits, which the base version of Qwen 2.5 VL struggles with. We will then inspect the dataset, annotate it,…

  • Why I stopped Using Cursor and Reverted to VSCode

    Why I stopped Using Cursor and Reverted to VSCode Introduction In December 2024, I wrote an article sharing my experience using VSCode (GitHub Copilot) and Cursor (Claude 3.5 Sonnet) from the perspective of a Data Scientist. Should you switch from VSCode to Cursor? I concluded the article by stating: After using Cursor for the past two…

  • How Would I Learn to Code with ChatGPT if I Had to Start Again

    How Would I Learn to Code with ChatGPT if I Had to Start Again Coding has been a part of my life since I was 10. From modifying HTML & CSS for my Friendster profile during the simple internet days to exploring SQL injections for the thrill, building a three-legged robot for fun, and lately…

  • Why Are Convolutional Neural Networks Great For Images?

    Why Are Convolutional Neural Networks Great For Images? The Universal Approximation Theorem states that a neural network with a single hidden layer and a nonlinear activation function can approximate any continuous function.  Practical issues aside, such that the number of neurons in this hidden layer would grow enormously large, we do not need other network architectures. A simple…

  • Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning

    Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning You see a math formula you don’t immediately understand. Your instinct? Stop reading. Don’t. That’s exactly what I told myself when I started reading Probabilistic Machine Learning – An Introduction by Kevin P. Murphy. And it was absolutely worth it. It changed how I…

  • Reinforcement Learning from One Example?

    Reinforcement Learning from One Example? Prompt engineering alone won’t get us to production. Fine-tuning is expensive. And reinforcement learning? That’s been reserved for well-funded labs with massive datasets until now. New research from Microsoft and academic collaborators has overturned that assumption. Using Reinforcement Learning with Verifiable Rewards (RLVR) and just a single training example, researchers…

  • From FOMO to Opportunity: Analytical AI in the Era of LLM Agents

    From FOMO to Opportunity: Analytical AI in the Era of LLM Agents Are you feeling “fear of missing out” (FOMO) when it comes to LLM agents? Well, that was the case for me for quite a while. In recent months, it feels like my online feeds have been completely bombarded by “LLM Agents”: every other…

  • If I Wanted to Become a Machine Learning Engineer, I’d Do This

    If I Wanted to Become a Machine Learning Engineer, I’d Do This If I wanted to become a machine learning engineer again, this is the exact process I would follow. Let’s get into it! First become a data scientist or software engineer I’ve said it before, but a machine learning engineer is not exactly an entry-level position.…

  • How to Ensure Your AI Solution Does What You Expect iI to Do

    How to Ensure Your AI Solution Does What You Expect iI to Do Generative AI (GenAI) is evolving fast — and it’s no longer just about fun chatbots or impressive image generation. 2025 is the year where the focus is on turning the AI hype into real value. Companies everywhere are looking into ways to…

  • LLM Evaluations: from Prototype to Production

    LLM Evaluations: from Prototype to Production Evaluation is the cornerstone of any machine learning product. Investing in quality measurement delivers significant returns. Let’s explore the potential business benefits. As management consultant and writer Peter Drucker once said, “If you can’t measure it, you can’t improve it.” Building a robust evaluation system helps you identify areas…

  • Choose the Right One: Evaluating Topic Models for Business Intelligence

    Choose the Right One: Evaluating Topic Models for Business Intelligence Topic models are used in businesses to classify brand-related text datasets (such as product and site reviews, surveys, and social media comments) and to track how customer satisfaction metrics change over time. There is a myriad of recent topic models one can choose from: the…

  • Predicting the NBA Champion with Machine Learning

    Predicting the NBA Champion with Machine Learning Every NBA season, 30 teams compete for something only one will achieve: the legacy of a championship. From power rankings to trade deadline chaos and injuries, fans and analysts alike speculate endlessly about who will raise the Larry O’Brien Trophy. But what if we could go beyond the hot…

  • How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI’s simple-evals

    How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI’s simple-evals The recent launch of the DeepSeek-R1 model sent ripples across the global AI community. It delivered breakthroughs on par with the reasoning models from Meta and OpenAI, achieving this in a fraction of the time and at a significantly lower cost. Beyond…

  • Exporting MLflow Experiments from Restricted HPC Systems

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