Foundations

ML Fundamentals

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

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ML Maths Basics
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Manipulate vectors and matrices
Supervised Learning
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Train decision trees, random forests, and neural nets
Unsupervised Learning
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Apply k-means and DBSCAN clustering
ML Pipelines
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Engineer features and handle missing data
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Free Models as Test Doubles: A Dev/Prod Split That Saves Real Money
Dev.to · Casey Li 📐 ML Fundamentals ⚡ AI Lesson 5h 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 1d 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 1d 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 2d 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 2d 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 2d 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 2d 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 3d 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 3d 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 3d 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 3d 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 3d 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 3d 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 3d 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 4d 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 4d 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 4d 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 4d 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 5d 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 5d 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 5d 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 5d 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 5d 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 6d 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 6d 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 6d 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 6d 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 1w 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 1w 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 2w 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 2w 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 2w 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 2w 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 2w 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 2w 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....
A Pest test that asserts nothing: toContain takes needles, not a message
Dev.to · Michael Yousrie 📐 ML Fundamentals ⚡ AI Lesson 2w ago
A Pest test that asserts nothing: toContain takes needles, not a message
I added a test last week that was doing absolutely nothing, and it passed every time I ran it. The...
Your AI Model Is 99% Accurate — So Why Is It Still Failing? 🤖
Dev.to · Probal Dhali 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Your AI Model Is 99% Accurate — So Why Is It Still Failing? 🤖
Machine Learning has a number that everyone loves to see: accuracy. You train your model, run the...
Why MCP Servers Need Verification Before Production | MCP Workbench
Dev.to · Jaypee 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Why MCP Servers Need Verification Before Production | MCP Workbench
Why MCP Servers Need Verification Before Production The Model Context Protocol (MCP) is...
Cuando tu clasificador parpadea: histéresis para señales que oscilan
Dev.to · Juan Carlos Isaza 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Cuando tu clasificador parpadea: histéresis para señales que oscilan
Una señal que cerca del umbral hace OK, CAÍDO, OK, CAÍDO dispara alertas o failovers en cada tembleque. La solución es vieja y elegante: no cambiar de estado ha
Programação Funcional
Dev.to · Yuri Peixinho 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Programação Funcional
Introdução A Programação Funcional tem raízes no cálculo lambda, formalizado pelo...
WeatherNext: DeepMind gana 24 horas de anticipación en huracanes
Dev.to · lu1tr0n 📐 ML Fundamentals ⚡ AI Lesson 2w ago
WeatherNext: DeepMind gana 24 horas de anticipación en huracanes
WeatherNext, el modelo de Google DeepMind publicado en Nature, predice trayectoria e intensidad de ciclones con un día extra de anticipación y ya ayud
Your CNN's Advantage Is One Assumption — and I Measured What Happens When It Breaks
Dev.to · Wesam Khallaf — Author of PyTorch From Ground Up 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Your CNN's Advantage Is One Assumption — and I Measured What Happens When It Breaks
A small convolutional network beats a plain flatten-and-feed-it-forward network by 7.0 points on...
Building a survey-based ML pipeline to find out what's really holding SEE and +2 graduates back
Dev.to · Sandip Subedi 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Building a survey-based ML pipeline to find out what's really holding SEE and +2 graduates back
Building a survey-based ML pipeline to find out what's really holding SEE and +2 graduates back The...
Building Smart Health API: A Production-Style REST API with CNN-Based Risk Prediction
Dev.to · Priyadharshiny J 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Building Smart Health API: A Production-Style REST API with CNN-Based Risk Prediction
By Priyadharshiny J — GitHub: priyadharshiny13 Overview Smart Health API is a backend...
Why I use reconstruction puzzles to keep interview algorithms fresh
Dev.to · clan chen 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Why I use reconstruction puzzles to keep interview algorithms fresh
The hardest part of algorithm interview prep was not learning binary search or two pointers for the...
zhao-cli: a free, deterministic breaking-change gate for dbt
Dev.to · allenhori 📐 ML Fundamentals ⚡ AI Lesson 2w ago
zhao-cli: a free, deterministic breaking-change gate for dbt
I kept hitting the same problem on almost every dbt project I worked on: a PR changes a column...
Model AI Seat Cost Against a Defined Workflow Outcome
Dev.to · TuanPK Builds 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Model AI Seat Cost Against a Defined Workflow Outcome
The plan price tells you what the vendor charges, not what the workflow costs. A small test...
300+ languages doesn't mean what you think: three tiers of code understanding
Dev.to · Na'aman Hirschfeld (Goldziher) 📐 ML Fundamentals ⚡ AI Lesson 2w ago
300+ languages doesn't mean what you think: three tiers of code understanding
Every tool in this space leads with a language count. basemind's README says 300+. Serena says...