ML Fundamentals

Data Scientist and ML Engineer roles are among the most competitive in tech right now, and the interview process reflects it. Hiring teams… Continue reading on

Rubric Dropout is a one-line fix borrowed from neuron dropout to mitigate reward hacking in Rubric-as-Reward RL. Continue reading on Medium »

Rubric Dropout is a one-line fix borrowed from neuron dropout to mitigate reward hacking in Rubric-as-Reward RL. Continue reading on Medium »

Want to learn Python, Data Science, Machine Learning, or NLP in a practical way? Continue reading on Medium »

After 3 years in the trenches, working under US university professors, building for multiple AI startups, and sitting across the table… Continue reading on Medi

Four things flip the moment you upgrade, and one of them quietly changes your numbers Continue reading on Medium »

Four things flip the moment you upgrade, and one of them quietly changes your numbers Continue reading on Medium »

Lessons from building a synthetic proof of concept combining predictive analytics, anomaly detection, grounded explanations… Continue reading on Medium »

Lessons from building a synthetic proof of concept combining predictive analytics, anomaly detection, grounded explanations… Continue reading on Medium »

Hi there, in this post i will explain the very basics of testing code for the data engineering domain using databricks, just a quick… Continue reading on Medium

How I combined Python libraries, automation, APIs, Excel, and AI into a practical workflow I could turn into a sellable service Continue reading on Medium »

In two dimensions the closest point in your data is far nearer than the farthest one. In a thousand dimensions it is barely closer at all… Continue reading on D

In two dimensions the closest point in your data is far nearer than the farthest one. In a thousand dimensions it is barely closer at all… Continue reading on D

Every time you hear about deep learning, ChatGPT, or image generators, there’s a neural network somewhere underneath. It’s the structure… Continue reading on Me

Anshul Parate · 5 min read Continue reading on Medium »

Machine learning (ML) is commonly divided into four main types, based on how the model learns from data. Continue reading on Medium »

# The Test Suite Broke Because Someone Did Their Job Continue reading on Medium »

Six months sounds surprisingly short when we’re talking about a field as large as Artificial Intelligence. Continue reading on Medium »

KNN (K-Nearest Neighbors) is a supervised and distance-based machine learning algorithm that makes predictions based on the K closest data… Continue reading on

What if a neural network had a little database it could read from and write to? Continue reading on Ilya Sutskever’s 30 Foundational Papers of AI, Explained »

Part 4 of 4: what to actually log, why eval scores go stale, running automated evals on live traffic, and the dashboards that catch a… Continue reading on Mediu

Sparse upcycling is a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense… Continue reading on Me

Sparse upcycling is a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense… Continue reading on Me

Sparse upcycling is a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense… Continue reading on Me

Classifying, extracting, and validating high volumes of documents is a challenge across banking, insurance, healthcare, and the public sector. See how a mid-siz

An end-to-end mini MLOps architecture with PSI + KS tests, FastAPI, Prometheus, MLflow, and auto-retrain Continue reading on Medium »

If you’ve been running a vector database at any real scale, you’ve probably hit the moment where you open your cloud bill and just… stare… Continue reading on M

I’ve been thinking a lot about the transition in data for AI training recently. Continue reading on Medium »

A few months ago I was running a long-context RAG setup locally, feeding a model a big pile of documents and asking it questions across… Continue reading on Med

I dataset alla base dei modelli di Machine Learning (ML) sono spesso presentati come substrati tecnici neutri e oggettivi. Continue reading on Medium »

A gentle deep-dive into link analysis — the algorithm that turned a chaotic web into a ranked one Continue reading on Medium »

Balancing text extraction accuracy with strict data privacy is a persistent engineering conflict in identity verification. Continue reading on Medium »

Python’s four essential collection types with practical examples, use cases, differences, and beginner-friendly explanations. Continue reading on Artificial Int

How we used machine learning to bring greater consistency, transparency and speed to data issue triage. Continue reading on AI at Lloyds Banking Group »

A simple, step-by-step look at what one neuron really does — using real numbers, not just theory. Continue reading on Medium »

A Day in the Life of an ML Engineer: What the Job Actually Looks Like Versus What People Imagine Continue reading on Medium »

Building a measurement harness to understand whether KV cache compression can make local LLM inference more energy efficient. Continue reading on Medium »

Full fine-tuning of a 7B model needs roughly 112GB for standard Adam-style optimization, before activations. That’s past a single H100’s… Continue reading on Me

We Already Know What DFS Does Continue reading on Generative AI »

We Already Know What DFS Does Continue reading on Generative AI »

Businesses generate more data than ever through websites, mobile applications, customer interactions, transactions, connected devices, and… Continue reading on

Built for a Site With No WiFi Continue reading on Medium »

A beginner-friendly guide to understanding how optimization algorithms train neural networks, from basic Gradient Descent to the powerful… Continue reading on M

A beginner-friendly guide to understanding how optimization algorithms train neural networks, from basic Gradient Descent to the powerful… Continue reading on M

A beginner-friendly guide to understanding how optimization algorithms train neural networks, from basic Gradient Descent to the powerful… Continue reading on M

The paper investigates post-hoc compression of long chain-of-thought reasoning traces generated by large teacher models before knowledge… Continue reading on Me

The paper investigates post-hoc compression of long chain-of-thought reasoning traces generated by large teacher models before knowledge… Continue reading on Me

Baca Versi Bahasa Indonesia di Sini Continue reading on COMPFEST »