Foundations

ML Fundamentals

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

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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
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Building Strong ML Foundations: Chapter 2 - Classification is Now Live
Dev.to · zkaria gamal 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Building Strong ML Foundations: Chapter 2 - Classification is Now Live
A few weeks ago I published Chapter 1 of my hands-on AI tutorial series, focused on Regression....
TensorFlow vs PyTorch: The Real Difference Isn't Accuracy
Dev.to · Rakshath 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
TensorFlow vs PyTorch: The Real Difference Isn't Accuracy
A hands-on comparison of performance, flexibility, and developer experience using CIFAR-10 A few...
64. Precision and Recall: Beyond Accuracy
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
64. Precision and Recall: Beyond Accuracy
Last post you saw that accuracy can be 95% while your model catches zero fraud. Precision and recall...
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...
From Developer Laptops to Isolated Containers — Enterprise MCP Infrastructure with MCPNest
Dev.to · Ricardo Rodrigues 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
From Developer Laptops to Isolated Containers — Enterprise MCP Infrastructure with MCPNest
The Problem The MCP ecosystem is growing fast. Anthropic, Microsoft, Google, AWS, and...
What 123 million simulated CS2 case openings taught me about modeling RNG
Dev.to · graysonwerner100-commits 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
What 123 million simulated CS2 case openings taught me about modeling RNG
I run case-sim.com, a free CS2 case opening simulator. As of this week the global counter ticked past...
De investigador postdoctoral a Data Scientist: dos proyectos reales, una transición
Dev.to · Diego Herrera Ochoa 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
De investigador postdoctoral a Data Scientist: dos proyectos reales, una transición
Predecir quién gana un partido de tenis. Demostrar que el aceite de oliva de alta montaña es...
Your Recursion Is Lying to You
Dev.to · Gabor Koos 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
Your Recursion Is Lying to You
Recursion is one of those ideas developers learn early and trust for years. If the recursive step is...
How we almost wrote off 3 models as broken — the thinking-mode tax
Dev.to · Vilius 📐 ML Fundamentals ⚡ AI Lesson 1mo ago
How we almost wrote off 3 models as broken — the thinking-mode tax
How we almost wrote off 3 models as broken — the thinking-mode tax By Vilius Vystartas |...
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...
Am I Ready for FAANG? A Better Test Than Solving More LeetCode
Dev.to · Prakhar Srivastava 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Am I Ready for FAANG? A Better Test Than Solving More LeetCode
You've solved 200 problems. Mediums you've already seen take fifteen minutes. The next one you...
TinyML on microcontrollers: from prototype to production
Dev.to · Marco 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
TinyML on microcontrollers: from prototype to production
What changes when a TinyML demo becomes a product: data quality, quantization, memory, latency, OTA, monitoring and lifecycle.
Escaping the tutorial trap and starting my ML training arc
Dev.to · VishnuRv 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Escaping the tutorial trap and starting my ML training arc
You ever start watching an anime or a series on episode 12 and just sit there wondering wtf is going...
🦎 Project Chameleon: The Self-Describing Data Engine powered by Gemma 4 🧠
Dev.to · prakashmehta@97 📐 ML Fundamentals ⚡ AI Lesson 2mo 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...
How Deep Learning Architectures Evolved — From DNNs to Transformers
Dev.to · zeromathai 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
How Deep Learning Architectures Evolved — From DNNs to Transformers
Deep learning architectures are not random model names. DNN, CNN, RNN, and Transformer each appeared...
61. K-Nearest Neighbors: Judge by Your Company
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 2mo 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 2mo ago
60. Support Vector Machines: Drawing the Perfect Boundary
Most classification algorithms find a boundary that separates classes. SVM finds the boundary that is...
Python Performance Analysis
Dev.to · Deepak Prasad 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Python Performance Analysis
When I first started using pandas for data analysis, I started using loops. However, loops are time...
59. XGBoost: The Algorithm That Wins Competitions
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
59. XGBoost: The Algorithm That Wins Competitions
If you've spent any time on Kaggle, you've seen XGBoost win. Over and over. Structured data...
tierKV: A Distributed KV Cache That Makes Evicted Blocks Faster to Restore Than GPU Cache Hits
Dev.to · prasanna kanagasabai 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
tierKV: A Distributed KV Cache That Makes Evicted Blocks Faster to Restore Than GPU Cache Hits
The Problem When your GPU's KV cache fills up, inference engines evict blocks and discard...
🚀 Just built a beginner-friendly AI tool called Mini AI Auto Trainer 🤖
Dev.to · Deepak | DeeStudio 📐 ML Fundamentals ⚡ AI Lesson 2mo 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 ✅...
Why Field-Level OCR Breaks Down in Real Expense Reimbursement Workflows
Dev.to · CY Ong 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Why Field-Level OCR Breaks Down in Real Expense Reimbursement Workflows
For engineering teams building document ingestion pipelines across fintech, SaaS, and ecommerce...
Building Smaller Graph Neural Networks for Edge Healthcare Systems
Dev.to · Swapin Vidya 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Building Smaller Graph Neural Networks for Edge Healthcare Systems
How I explored INT8 quantization, biological graphs, and CPU-only inference using PyTorch...
Building Sparrow — notes from writing a small Proof-of-Work blockchain in 2026
Dev.to · jian 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Building Sparrow — notes from writing a small Proof-of-Work blockchain in 2026
I built a small Proof-of-Work cryptocurrency called Sparrow (SPW) as a long-running side project....
Internal Architecture of Neural Networks
Dev.to · Ganesh Kumar 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Internal Architecture of Neural Networks
Hello, I'm Ganesh. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free,...
Why Your Non-Significant Benchmark Result Might Be a Power Problem (Not a Model Problem)
Dev.to · Beamlaka 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Why Your Non-Significant Benchmark Result Might Be a Power Problem (Not a Model Problem)
In Week 11, Tenacious-Bench reported: Delta A = -2.34 pts, 95% CI [-11.09, +6.20], p = 0.71 (not...
AcousticBrainz Alternative in 2026: The Honest Insider's Guide
Dev.to · Freqblog 📐 ML Fundamentals ⚡ AI Lesson 2mo 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
# Why "drift_score = 0.0" Is Not Yet Evidence of Semantic Stability — and What Your n=251 vs cap=200 Mismatch Actually Costs by: Eyoel Nebiyu
Dev.to · Eyoel Nebiyu 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
# Why "drift_score = 0.0" Is Not Yet Evidence of Semantic Stability — and What Your n=251 vs cap=200 Mismatch Actually Costs by: Eyoel Nebiyu
Repo under interrogation: Heban-7/Data-Contract-Enforcer Files in scope: report_final_pdf_ready.md,...
The MCP Configuration Guide Nobody Wrote (But Every Dev Needs)
Dev.to · Aria13 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
The MCP Configuration Guide Nobody Wrote (But Every Dev Needs)
The complete setup, configuration & orchestration guide for Model Context Protocol - stop running 100 broken processes, start running 10 powerful ones
A beginner path-guide to Python
Dev.to · Kinyanjui 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
A beginner path-guide to Python
Python is one of the most popular programming language in the world. Created by Guido Van Rossum in...
Beyond Annotation: The AI Pipeline that Redefines Medical Imaging
Dev.to · CapeStart 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Beyond Annotation: The AI Pipeline that Redefines Medical Imaging
Why AI in Medical Imaging Depends on High-Quality Data Pipelines In today’s world, AI is...
I built a local AI tool to analyze why I abandon 90% of my GitHub repos (Source Code included)
Dev.to · beatsprom 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
I built a local AI tool to analyze why I abandon 90% of my GitHub repos (Source Code included)
If you look at my GitHub, it looks like a graveyard of "next big things." I have over 20 private...
🤔 Questions Only Developers Will Lose Sleep Over (And Secretly Love Answering)
Dev.to · Hanzla Baig 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
🤔 Questions Only Developers Will Lose Sleep Over (And Secretly Love Answering)
🤔 Questions Only Developers Will Lose Sleep Over (And Secretly Love Answering) By a Developer, For...
From Score to Workflow: Turning STEM BIO-AI Into a Local Audit System
Dev.to · Kwansub Yun 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
From Score to Workflow: Turning STEM BIO-AI Into a Local Audit System
Earlier in this series, I wrote about why bio/medical AI repositories need more than benchmarks,...
What is MCP? My Beginner's Guide to Model Context Protocol
Dev.to · Uma Baleboyina 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
What is MCP? My Beginner's Guide to Model Context Protocol
Introduction When I started learning Generative AI, one of the first things I came across was...
58. Random Forest: Why One Tree Isn't Enough
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 2mo 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...
Understanding Text to Binary Conversion (With Examples)
Dev.to · Tarun Dudhatra 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Understanding Text to Binary Conversion (With Examples)
Understanding Text to Binary Conversion (With Examples) If you're learning programming or...
How to Make xt850 Match xt 850
Dev.to · Sergey Nikolaev 📐 ML Fundamentals ⚡ AI Lesson 2mo 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...
Model Showdown Round 2: Adding Gemma, Kimi, and 579 GB of Stubborn Optimism
Dev.to · Rob 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Model Showdown Round 2: Adding Gemma, Kimi, and 579 GB of Stubborn Optimism
At the end of Round 1, we promised a rematch. More models. Fixed settings. Harder questions about...
57. Decision Trees: The AI That Plays 20 Questions
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 2mo 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...
Python Celery Task Queues for Video Metadata Processing
Dev.to · ahmet gedik 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Python Celery Task Queues for Video Metadata Processing
Use Celery with Redis to validate thumbnails, detect content language for multilingual regions inclu
How We Refactored 100k LOC of Python 3.15 to Rust 1.86 and Cut CPU Usage by 55% at a Fintech Startup
Dev.to · ANKUSH CHOUDHARY JOHAL 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
How We Refactored 100k LOC of Python 3.15 to Rust 1.86 and Cut CPU Usage by 55% at a Fintech Startup
How We Refactored 100k LOC of Python 3.15 to Rust 1.86 and Cut CPU Usage by 55% at a Fintech...
Building AI Systems for Healthcare: My Journey into Applied Machine Learning and Software Engineering
Dev.to · enochlabs 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Building AI Systems for Healthcare: My Journey into Applied Machine Learning and Software Engineering
🧠 I Stopped Thinking in Machine Learning Models and Started Thinking in Systems (Here’s Why) And it...
1minMLOps #1 : What is MLOps and why should you care?
Dev.to · Mohamed Arbi 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
1minMLOps #1 : What is MLOps and why should you care?
If you've ever trained a beautiful model in a Jupyter notebook, watched the metrics shine, and then...
Shipping Paid MCP Tools on Base Mainnet: the Build Pattern and What I Got Wrong
Dev.to · Randy Rockwell 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Shipping Paid MCP Tools on Base Mainnet: the Build Pattern and What I Got Wrong
A couple of months ago I shipped ForgePoint Signal — a regulatory monitoring MCP server with x402...
Stop tuning one model. Route per workload.
Dev.to · TokenHub 📐 ML Fundamentals ⚡ AI Lesson 2mo 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...
I built a World Cup 2026 bracket predictor for the new 48-team format
Dev.to · Mark 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
I built a World Cup 2026 bracket predictor for the new 48-team format
I built a World Cup 2026 bracket predictor for the new 48-team format The 2026 FIFA World...
Beyond Monitoring: Building AI-Powered Predictive Observability for Retail Data Pipelines published
Dev.to · Arunkumar Amaran 📐 ML Fundamentals ⚡ AI Lesson 2mo ago
Beyond Monitoring: Building AI-Powered Predictive Observability for Retail Data Pipelines published
How we stopped reacting to broken pipelines and started predicting failures before they hit production — lessons from two years of building at enterprise scale