Foundations

ML Fundamentals

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

33,160
lessons
Skills in this topic
View full skill map →
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 (20,955) Articles (9965)Blog Posts (4030)Tutorials (1833)Research Papers (4684)News (443)
Differential Testing Is the Proof Your Extraction Is Missing
Dev.to · Dakota Huang 📐 ML Fundamentals ⚡ AI Lesson 3d ago
Differential Testing Is the Proof Your Extraction Is Missing
Most extractions break at merge, not at edit time. The build passes. The tests pass. The function...
The Score Is a Symptom: A Diagnostic Tree for Free-Model Evals
Dev.to · Taylor Lin 📐 ML Fundamentals ⚡ AI Lesson 4d ago
The Score Is a Symptom: A Diagnostic Tree for Free-Model Evals
A free model scores 30% on your eval. The obvious conclusion: the model is weak. Swap it, and the...
Your Eval Set Is Rotting: A Theme-Fingerprint Drift Check
Dev.to · Emery Li 📐 ML Fundamentals ⚡ AI Lesson 4d ago
Your Eval Set Is Rotting: A Theme-Fingerprint Drift Check
Your evaluation set has an expiration date. Last month it helped you pick a model. This month it is...
What I Learned Building a Real-Time Thin-Film Thickness Monitoring Pipeline
Dev.to · raymond zhao 📐 ML Fundamentals ⚡ AI Lesson 4d ago
What I Learned Building a Real-Time Thin-Film Thickness Monitoring Pipeline
If you've ever had to instrument a coating line, you know the hard part isn't the optics. It's...
The Best Anomaly Detector I Know Optimizes Nothing
Dev.to · Nishant Banginwar 📐 ML Fundamentals ⚡ AI Lesson 4d ago
The Best Anomaly Detector I Know Optimizes Nothing
Classic Machine Learning Through the Eyes of an SRE — Part 9: Isolation Forest The algorithm in one...
Lists in Python for Beginners
Dev.to · Vijayan Chakravarthi 📐 ML Fundamentals ⚡ AI Lesson 4d ago
Lists in Python for Beginners
A List can be considered as a dynamic array. It is denoted by [ ]. The values inside a list are...
Deploying ClearML as an Azure ML Alternative
Dev.to · Sanskriti Harmukh 📐 ML Fundamentals ⚡ AI Lesson 5d ago
Deploying ClearML as an Azure ML Alternative
Azure Machine Learning ties experiment tracking, pipelines, and model serving to Azure-specific APIs...
Measure Every Token: A SQLite-Backed Call Tracker for Free Model Endpoints
Dev.to · Dakota Huang 📐 ML Fundamentals ⚡ AI Lesson 6d ago
Measure Every Token: A SQLite-Backed Call Tracker for Free Model Endpoints
Free model endpoints are not free of mystery. You get a response, but no dashboard. No latency...
mcp-tool-sanitizer v0.1.0: Making the MCP approval-view match the bytes the model gets
Dev.to · Fenix 📐 ML Fundamentals ⚡ AI Lesson 6d ago
mcp-tool-sanitizer v0.1.0: Making the MCP approval-view match the bytes the model gets
mcp-tool-sanitizer v0.1.0: Making the MCP approval-view match the bytes the model gets A...
A Higher Pass Rate Can Mean a Worse Model. The Math Is Simpson's Paradox.
Dev.to · Maya Andersson 📐 ML Fundamentals ⚡ AI Lesson 6d ago
A Higher Pass Rate Can Mean a Worse Model. The Math Is Simpson's Paradox.
We shipped a model update last quarter that moved our aggregate pass rate from 81.2% to 83.6%....
A judge that agrees with your humans 92 percent of the time can be at 60 percent where the gate actually decides
Dev.to · Maya Andersson 📐 ML Fundamentals ⚡ AI Lesson 6d ago
A judge that agrees with your humans 92 percent of the time can be at 60 percent where the gate actually decides
TL;DR: Judge-human agreement is almost always reported as one number over a whole validation set....
Six Posts on Ensembles and Tuning, and the Uncomfortable Thing They All Turned Out to Be About
Dev.to · Sachin Kr. Rajput 📐 ML Fundamentals ⚡ AI Lesson 6d ago
Six Posts on Ensembles and Tuning, and the Uncomfortable Thing They All Turned Out to Be About
The One-Line Summary: I set out to write six posts comparing ensemble and tuning methods, and every...
Decision Trees vs Random Forests: When Should You Use Which?
Dev.to · AheadMint Official 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Decision Trees vs Random Forests: When Should You Use Which?
Choosing between a Decision Tree and a Random Forest is one of the first architectural decisions in...
Free Models as Test Doubles: A Dev/Prod Split That Saves Real Money
Dev.to · Casey Li 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Free Models as Test Doubles: A Dev/Prod Split That Saves Real Money
The most expensive place to call a paid model API is your own laptop during debugging. Every retry,...
I Built a Free CLI + MCP Server for GEO Audits - Here is What 1,200 Sites Taught Me
Dev.to · L D (一π狐言) 📐 ML Fundamentals ⚡ AI Lesson 1w ago
I Built a Free CLI + MCP Server for GEO Audits - Here is What 1,200 Sites Taught Me
I spent two months building a free GEO (Generative Engine Optimization) audit tool with a CLI and an...
Adaptive compute techniques yield significant inference speedups across models
Dev.to · Papers Mache 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 1w ago
Adaptive compute techniques yield significant inference speedups across models
FlashMorph slashes the cost of designing hybrid attention models, needing only 20 M tokens and...
The cheapest model on my plan loses every benchmark. It still beats models charging 14x more.
Dev.to · Michael Amachree 📐 ML Fundamentals ⚡ AI Lesson 1w ago
The cheapest model on my plan loses every benchmark. It still beats models charging 14x more.
I ran the $0.14 model against the $0.44 model expecting a close fight. It lost 4-0. Then I looked at what it does to everything priced in between.
Blending and Voting: Four Noses, One Bottle, and the Blender Who Graded His Own Homework
Dev.to · Sachin Kr. Rajput 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Blending and Voting: Four Noses, One Bottle, and the Blender Who Graded His Own Homework
The One-Line Summary: Voting has no learned parameters, so it cannot overfit and it cannot lie to...
GC-OPD: Reconciling Teacher Likelihood with Verified Task Success
Dev.to · Prabhakar Chaudhary 📐 ML Fundamentals ⚡ AI Lesson 1w ago
GC-OPD: Reconciling Teacher Likelihood with Verified Task Success
The mismatch inside standard on-policy distillation On-policy distillation trains a...
JX N-Body Engine 0.1.0: Arbitrary-Precision Python and Numerical Validation
Dev.to · Lino Avila 📐 ML Fundamentals ⚡ AI Lesson 1w ago
JX N-Body Engine 0.1.0: Arbitrary-Precision Python and Numerical Validation
A Newtonian N-body engine built around a sixth-order Yoshida integrator, an independent Decimal...
Beyond the Vector: Why Graph Neural Networks are the Strategic Choice for Enterprise Generative AI on GCP
Dev.to · Kutluk Atalay 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Beyond the Vector: Why Graph Neural Networks are the Strategic Choice for Enterprise Generative AI on GCP
In the current epoch of Artificial Intelligence, the industry remains singularly preoccupied with the...
Don't Trust the First Token: A Streaming Latency Autopsy on Free Model Servers
Dev.to · Jordan Huang 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Don't Trust the First Token: A Streaming Latency Autopsy on Free Model Servers
Streaming changes everything. Or so I thought. Then I measured it. The first token is a...
Go 1.27's SIMD ties with NumPy until the data fits in cache
Dev.to · Efrain Garay 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Go 1.27's SIMD ties with NumPy until the data fits in cache
I measured Go 1.27's experimental simd package against NumPy. They tie out of cache and lose inside it, and the reason is not the language.
Go 1.27's SIMD ties with NumPy until the data fits in cache
Dev.to · Efrain Garay 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Go 1.27's SIMD ties with NumPy until the data fits in cache
I measured Go 1.27's experimental simd package against NumPy. They tie out of cache and lose inside it, and the reason is not the language.
Computing WHO growth percentiles on-device
Dev.to · Roman Koropets 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Computing WHO growth percentiles on-device
Every baby tracker shows growth percentiles. "Your daughter is in the 72nd percentile for weight." It...
Model Routing in Production: Cheap First, Escalate on Doubt
Dev.to · sagar jain 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Model Routing in Production: Cheap First, Escalate on Doubt
Route most requests to the cheapest model that passes your evals, and send a request to the expensive model only when a cheap, checkable signal says the...
How I built a crypto signal generator that beat fixed-weight strategies by 37% Sharpe
Dev.to · ömer faruk aydın 📐 ML Fundamentals ⚡ AI Lesson 1w ago
How I built a crypto signal generator that beat fixed-weight strategies by 37% Sharpe
Combining 13 technical indicators in an XGBoost model with Bayesian-optimized hyperparameters - a complete Python pipeline.
Part 1 — What Actually Happens When Code Runs
Dev.to · Alok Kumar 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Part 1 — What Actually Happens When Code Runs
When we write: const result = add(10, 20); Enter fullscreen mode Exit fullscreen...
Compared Quantization Levels: Q4 vs Q8 vs FP16 on llama3.2:1b
Dev.to · Nerav Doshi 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Compared Quantization Levels: Q4 vs Q8 vs FP16 on llama3.2:1b
Context: A model's weights — the numbers it uses to reason — are normally stored at high precision,...
Point-in-Time Fundamentals for Numerai Signals: Killing Lookahead in Your Feature Join
Dev.to · Christian Pichichero 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Point-in-Time Fundamentals for Numerai Signals: Killing Lookahead in Your Feature Join
If you build features for Numerai Signals from fundamentals, the single most common way to silently...
Choosing the Right GPU for Your Model — A Sizing Method, Not a Guess
Dev.to · Josef Doornink 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Choosing the Right GPU for Your Model — A Sizing Method, Not a Guess
Choosing the Right GPU for Your Model — A Sizing Method, Not a Guess OK,...
Who your model works with matters more than which model you picked
Dev.to · Tom Jones 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Who your model works with matters more than which model you picked
The short version, for anyone who does not benchmark models for a living Every few weeks a...
Predicting CPU Spikes
Dev.to · Shashi Bhushan Savarn 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Predicting CPU Spikes
Predictive System Health Checks: What I Learned Testing ARIMA, SARIMA, and Prophet on Infrastructure...
From Zero to Hero: Preparing for FAANG Interviews in 3 Months – A Journey Inspired by *The Lord of the Rings*
Dev.to · Timevolt 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From Zero to Hero: Preparing for FAANG Interviews in 3 Months – A Journey Inspired by *The Lord of the Rings*
The Quest Begins (The "Why") Honestly, I used to stare at a blank editor and feel like...
Make the Model Show Its Work
Dev.to · Serguey Asael Shinder 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Make the Model Show Its Work
Don't just ask for the answer. Ask how it got there. A model will hand you a conclusion with total...
Building Fault-Tolerant, Event-Driven Kafka Pipelines in Go: Reliable Reprocessing & Dead Letter Queues
Dev.to · Samuel Umoh 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Building Fault-Tolerant, Event-Driven Kafka Pipelines in Go: Reliable Reprocessing & Dead Letter Queues
A practical guide to building reliable event-driven systems in Go using Apache Kafka. Learn how to...
A Free Server Is Enough to Test a New Model Before You Trust It
Dev.to · Quinn Li 📐 ML Fundamentals ⚡ AI Lesson 2w ago
A Free Server Is Enough to Test a New Model Before You Trust It
You do not need a large budget to find out whether a freshly announced model fits your system. A...
How We Hardened a Multilingual TypeScript Text Filter Against Real Bypasses and False Positives
Dev.to · Ashkan Ahmadi 📐 ML Fundamentals ⚡ AI Lesson 2w ago
How We Hardened a Multilingual TypeScript Text Filter Against Real Bypasses and False Positives
Text filtering looks deceptively simple when the first version works on isolated examples. Give a...
Replay Your Last Ten Bugfixes Before You Trust a New Coding Model
Dev.to · Quinn Sun 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Replay Your Last Ten Bugfixes Before You Trust a New Coding Model
Consider a small team that sees two model releases in the same week. One is DeepSeek-V4-Pro-0813,...
From API to GPU, Week 5: Tensors, the Data Structure Behind Every Model
Dev.to · Dinesh Kumar Ramasamy 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From API to GPU, Week 5: Tensors, the Data Structure Behind Every Model
Phase 2 of 8: Enough ML to understand inference. Week 5 of 32. Phase 1 was about running models....
A Free Model Endpoint Replied Twice, Then Went Silent. The Fix Was a C++ Replay Envelope, Not Retries
Dev.to · Finley Zhou 📐 ML Fundamentals ⚡ AI Lesson 2w ago
A Free Model Endpoint Replied Twice, Then Went Silent. The Fix Was a C++ Replay Envelope, Not Retries
Late on a Tuesday, a C++ tooling team noticed their warning classifier was duplicating...
Why Making AI Answer Faster Is Worth $1.5 Billion
Dev.to · Alexander Kopylkov 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Why Making AI Answer Faster Is Worth $1.5 Billion
Keeping an AI model fast enough to use is turning out to be the expensive part of building...
Simon Willison's Blog 📐 ML Fundamentals ⚡ AI Lesson 3w ago
GitHub Models is now retired
GitHub Models is now retired I missed this news until today, when the GitHub Actions run for my simonw/research repository failed with this error message: GitHu
Build, Buy, or Call an API: How We Actually Decide
Dev.to · sagar jain 📐 ML Fundamentals ⚡ AI Lesson 3w ago
Build, Buy, or Call an API: How We Actually Decide
Clients ask me why we don't just build our own model. It's a fair question, and most of the time the honest answer is that building our own would be the slowest
Serving Models with TensorFlow Serving
Dev.to · Aviral Srivastava 📐 ML Fundamentals ⚡ AI Lesson 3w ago
Serving Models with TensorFlow Serving
Unleash Your AI: Serving Models Like a Pro with TensorFlow Serving So, you've poured your...
A Stroke Instead of a Picture: The Evolution of Recognition Paradigms as Exemplified by Speech and Handwritten Input
Dev.to · oleg kholin 📐 ML Fundamentals ⚡ AI Lesson 3w ago
A Stroke Instead of a Picture: The Evolution of Recognition Paradigms as Exemplified by Speech and Handwritten Input
The problem of speech recognition in contemporary artificial intelligence systems can be described as...
I Could Not Mentally Calculate Bitwise XOR in a Coding Interview — So I Built a Visual Calculator
Dev.to · dayu2333-jinyul 📐 ML Fundamentals ⚡ AI Lesson 3w ago
I Could Not Mentally Calculate Bitwise XOR in a Coding Interview — So I Built a Visual Calculator
After bombing a bitwise operations question, I built a free visual calculator that shows AND, OR, XOR, NOT, NAND, NOR, XNOR with binary alignment.
I measured his app with his own code. He measured my claim with his own corpus.
Dev.to · Li Zhuojun 📐 ML Fundamentals ⚡ AI Lesson 3w ago
I measured his app with his own code. He measured my claim with his own corpus.
This is part five of a series about pointing an append-only audit log at things that count tokens....