Foundations

ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

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Skills in this topic
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ML Maths Basics
beginner
Manipulate vectors and matrices
Supervised Learning
beginner
Train decision trees, random forests, and neural nets
Unsupervised Learning
intermediate
Apply k-means and DBSCAN clustering
ML Pipelines
intermediate
Engineer features and handle missing data
All Reads (3,268) Articles (1867)Blog Posts (733)Tutorials (544)Research Papers (120)News (4)
Retries and Idempotency in AI Pipelines: A Guide to Error Handling
Dev.to · Mustafa ERBAY 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Retries and Idempotency in AI Pipelines: A Guide to Error Handling
AI-based systems, especially pipelines running in production, constantly carry the risk of errors. I...
Tech Talks Weekly #104
Dev.to · Tech Talks Weekly 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Tech Talks Weekly #104
Happy Thursday 👋 and a warm welcome to Tech Talks Weekly #104! It's been a big week for the Java...
Replacing Lodash with Native ES2026: groupBy, fromAsync, toReversed, and 5 More
Dev.to · RAXXO Studios 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Replacing Lodash with Native ES2026: groupBy, fromAsync, toReversed, and 5 More
Lodash adds about 70KB minified, ES2026 covers most utility uses with zero...
DIY Search Engine: Complete Stack for Under €30
Dev.to · James 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
DIY Search Engine: Complete Stack for Under €30
Building a Self-Hosted Search Engine for Under €30/Month You don't need Google's...
Deploy a Real‑Time Object Detection API with YOLOv8 & FastAPI
Dev.to · Lich Priest 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Deploy a Real‑Time Object Detection API with YOLOv8 & FastAPI
Step‑by‑step guide to train a custom YOLOv8 model, containerize it with Docker, and serve low‑latency predictions via FastAPI
Why Do Data Teams Use AI to Write Code but Not to Monitor Pipelines?
Dev.to · Blaine Elliott 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Why Do Data Teams Use AI to Write Code but Not to Monitor Pipelines?
dbt's 2026 State of Analytics Engineering found that 72% of data teams prioritize AI-assisted coding, but only 24% prioritize AI-assisted pipeline management. T
68. PCA: Shrinking Data Without Losing Information
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
68. PCA: Shrinking Data Without Losing Information
You have 100 features. Most of them are correlated. Training is slow. Visualization is impossible....
[CryptoTradingBot] Analyzing 70 Live Trades of My Python Crypto Bot (v8.04 Post-Mortem)
Dev.to · rocketsquirreldev 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
[CryptoTradingBot] Analyzing 70 Live Trades of My Python Crypto Bot (v8.04 Post-Mortem)
I’ve been running version 8.04 of my Python-based crypto trading bot live for about a month. Instead...
The 70% Data-Prep Tax in AI Development (and How to Cut It in Half)
Dev.to · A3E Ecosystem 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
The 70% Data-Prep Tax in AI Development (and How to Cut It in Half)
Amershi et al. (2019) studied AI development workflows at Microsoft and reported a now-famous number:...
Simon Willison's Blog 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Quoting Andrew Quinn
One could say in the first quarter-century of my life, that while I was always fascinated by programming, I could never overcome the guilt of not really knowing
Three Detection Paradigms. One Dataset. One Result.
Dev.to · @alonso_isidoro 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Three Detection Paradigms. One Dataset. One Result.
Three Detection Paradigms. One Dataset. One Result. For the last 147 days I’ve been...
MCP tool schemas are contracts, not comments
Dev.to · Mads Hansen 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
MCP tool schemas are contracts, not comments
An MCP tool schema is not just documentation. It is part of the model’s operating environment. The...
How to Deploy a Machine Learning Project on AWS Using ECR, ECS Fargate, and EFS.
Dev.to · Tendong Brain Nkengafac 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
How to Deploy a Machine Learning Project on AWS Using ECR, ECS Fargate, and EFS.
A step-by-step walkthrough from Docker image to a live, serverless ML application running in the...
🦎 Project Chameleon: The Self-Describing Data Engine powered by Gemma 4 🧠
Dev.to · prakashmehta@97 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
🦎 Project Chameleon: The Self-Describing Data Engine powered by Gemma 4 🧠
This is a submission for the Gemma 4 Challenge: Build with Gemma 4 What I Built I built Auto-Dictate...
61. K-Nearest Neighbors: Judge by Your Company
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
61. K-Nearest Neighbors: Judge by Your Company
Every other algorithm we've covered so far actually learns something during training. It builds a...
60. Support Vector Machines: Drawing the Perfect Boundary
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
60. Support Vector Machines: Drawing the Perfect Boundary
Most classification algorithms find a boundary that separates classes. SVM finds the boundary that is...
🚀 Just built a beginner-friendly AI tool called Mini AI Auto Trainer 🤖
Dev.to · Deepak | DeeStudio 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
🚀 Just built a beginner-friendly AI tool called Mini AI Auto Trainer 🤖
The idea is simple: Upload a .csv file → the app automatically: ✅ detects the ML problem type ✅...
AcousticBrainz Alternative in 2026: The Honest Insider's Guide
Dev.to · Freqblog 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
AcousticBrainz Alternative in 2026: The Honest Insider's Guide
AcousticBrainz shut down in February 2022 but published the entire 7.5M-track dataset before going dark. What you actually lost, what's still usable from the du
58. Random Forest: Why One Tree Isn't Enough
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
58. Random Forest: Why One Tree Isn't Enough
You saw in the last post that decision trees overfit easily. Change a few training examples and the...
How to Make xt850 Match xt 850
Dev.to · Sergey Nikolaev 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
How to Make xt850 Match xt 850
TL;DR Since version 23.0.0, Manticore can make searches like xt850 match xt 850 using...
57. Decision Trees: The AI That Plays 20 Questions
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
57. Decision Trees: The AI That Plays 20 Questions
You've played 20 questions before. You think of something. Someone asks yes/no questions to figure...
Stop tuning one model. Route per workload.
Dev.to · TokenHub 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Stop tuning one model. Route per workload.
"What's the best model?" used to be a meaningful question. Today it has the wrong shape. The useful...
Building Mithridatium: Detecting Hidden Backdoors in ML Models
Dev.to · Pelumi Oluwategbe 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Building Mithridatium: Detecting Hidden Backdoors in ML Models
Building an open-source ML backdoor detection framework.
How I built an invoice extraction API that works on any PDF layout
Dev.to · Francesco Ira 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
How I built an invoice extraction API that works on any PDF layout
How I built an invoice extraction API that works on any PDF layout I kept running into the...
How much can a Front-end Developer learn about Machine Learning using only JavaScript?
Dev.to · Nomfundo Mtiyane 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
How much can a Front-end Developer learn about Machine Learning using only JavaScript?
Robot Playing Piano by Franck V on Unsplash: https://unsplash.com/photos/U3sOwViXhkY Machine Learning...
Avoiding Common Pitfalls in AI-Powered Predictive Analytics Implementation
Dev.to · Edith Heroux 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Avoiding Common Pitfalls in AI-Powered Predictive Analytics Implementation
Common Pitfalls in AI-Powered Predictive Analytics and How to Avoid Them As the e-commerce...
How to Implement AI-Powered Predictive Analytics in Your E-Commerce Strategy
Dev.to · jasperstewart 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
How to Implement AI-Powered Predictive Analytics in Your E-Commerce Strategy
Step-by-Step Guide to Implementing AI-Powered Predictive Analytics Implementing AI-Powered...
55. Multiple Regression: More Features, More Power (And More Ways to Break Things)
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
55. Multiple Regression: More Features, More Power (And More Ways to Break Things)
In the last post, you predicted house prices using one feature. One number in, one number out. Real...
Building an AI-Powered Prediction Engine for Racing Data: A Developer's Journey
Dev.to · Ali Can 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Building an AI-Powered Prediction Engine for Racing Data: A Developer's Journey
As developers, we are always looking for interesting datasets to test our machine learning skills....
AI-Based Agriculture Image Classification System using Deep Learning
Dev.to · Mogalluru Pavan 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
AI-Based Agriculture Image Classification System using Deep Learning
🌿 Introduction Agriculture plays a vital role in our daily life. Farmers often face...
LeetCode Solution: 74. Search a 2D Matrix
Dev.to · Vansh Aggarwal 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
LeetCode Solution: 74. Search a 2D Matrix
LeetCode 74: Search a 2D Matrix - Conquer the Grid with Binary Search! Hey fellow coders...
Machine Learning Developers: Why Most ML Projects Fail After the Model Stage
Dev.to · Dixit Angiras 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Machine Learning Developers: Why Most ML Projects Fail After the Model Stage
Training a model is easy. Getting 85–90% accuracy in a notebook? Also doable. But getting that model...
Unsupervised Machine Learning. K-Means & Hierarchical Clustering
Dev.to · Kelvin 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Unsupervised Machine Learning. K-Means & Hierarchical Clustering
Unsupervised machine learning is a branch of machine learning where models are trained on data...
Mutation Testing in .NET 10
Dev.to · Christian Alt-Wibbing 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Mutation Testing in .NET 10
Why your tests might be lying to you and how to catch them I've seen projects with 90%...
The Complete Guide to Document Parsing in 2026
Dev.to · Iteration Layer 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
The Complete Guide to Document Parsing in 2026
From regex to AI extraction — the complete guide to parsing documents programmatically. Methods, tools, and code examples.
Chapter 10: Multi-Head Attention and the MLP Block
Dev.to · Gary Jackson 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Chapter 10: Multi-Head Attention and the MLP Block
Run several attention heads in parallel on embedding slices, add a two-layer MLP for per-position computation, and assemble a transformer block.
Human in the Loop: Using Confidence Scores to Build Reliable Document Extraction
Dev.to · Iteration Layer 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Human in the Loop: Using Confidence Scores to Build Reliable Document Extraction
Why Fully Automated Extraction Fails Every document extraction project starts with the...
Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker
Dev.to · Tebogo Tseka 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker
Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker Every...
Python Roadmap for Beginners in 2026: Skills That Actually Make You Job-Ready
Dev.to · Zestminds Academy 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Python Roadmap for Beginners in 2026: Skills That Actually Make You Job-Ready
Python has been popular for many years, but in 2026, its value for beginners is even...
Why Data Quality is Becoming More Important Than Model Size in Modern AI Systems
Dev.to · Vishal Uttam Mane 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Why Data Quality is Becoming More Important Than Model Size in Modern AI Systems
For years, progress in artificial intelligence was closely tied to scaling laws, where increasing...
How to Migrate a Laravel 11 App to Next.js 15 with TypeScript 5.6 and Prisma 5.20
Dev.to · ANKUSH CHOUDHARY JOHAL 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
How to Migrate a Laravel 11 App to Next.js 15 with TypeScript 5.6 and Prisma 5.20
After 14 months of benchmarking 42 production Laravel 11 monoliths, we found that migrating to...
Building a Real‑Time Anomaly Detection Engine for Web Traffic
Dev.to · Patrick Onwujekwe 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Building a Real‑Time Anomaly Detection Engine for Web Traffic
Introduction Modern web applications live on the open internet, which means they’re constantly...
How I Built a Real-Time HTTP Traffic Anomaly Detector for a Cloud Storage Platform
Dev.to · Adjerese Precious 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
How I Built a Real-Time HTTP Traffic Anomaly Detector for a Cloud Storage Platform
Introduction Imagine you're running a cloud storage platform that serves thousands of...
Real-Time Anomaly Detection Engine for a Cloud Storage Platform
Dev.to · Timilehin Obalereko 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Real-Time Anomaly Detection Engine for a Cloud Storage Platform
I built a Python daemon that watches incoming HTTP traffic in real time, learns what "normal" looks...
From Pixels to Prescriptions: Engineering OCR Pipelines for Medical Report Simplification Using MongoDB
Dev.to · Kotha Deepak Reddy 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
From Pixels to Prescriptions: Engineering OCR Pipelines for Medical Report Simplification Using MongoDB
Team Members @k_sidharthareddy_15 | @k-deepak-544 | @nupur_madhrey_07 | @avika_kashyap |...
The Sous Chef Who Guesses in Batches
Dev.to · Bongho Tae 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
The Sous Chef Who Guesses in Batches
When Waiting Becomes the Problem You are sitting in a restaurant, watching the kitchen...
Choosing the Right Model (Not the Best One)
Dev.to · Siddhartha Reddy 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Choosing the Right Model (Not the Best One)
The best model is rarely the right model. And chasing it is one of the biggest mistakes in...
#08, It's Not That Hard~ Conditionals and Loops (Chapter 04, Sec 01, 02)
Dev.to · Hanna 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
#08, It's Not That Hard~ Conditionals and Loops (Chapter 04, Sec 01, 02)
Textbook: Self-Study Java (by Shin Yong-kwon) Sections: Chapter 04 Sec 01, Sec 02, Chapter Review...