Foundations

ML Fundamentals

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

11,987
lessons
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
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Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Your Model’s 90% Accuracy Is Lying to You
The most dangerous ML metric is one that looks right — and this is exactly what that failure looks like in production. Continue reading on Medium »
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The Complete Guide to Document Parsing in 2026
Dev.to · Iteration Layer 📐 ML Fundamentals ⚡ AI Lesson 2w 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.
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From Jupyter Notebook to Production API in 30 Minutes (FastAPI + Render)
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From Jupyter Notebook to Production API in 30 Minutes (FastAPI + Render)
Stop keeping your models in notebooks. Deploy them as real APIs that anyone can call. Continue reading on Medium »
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From Jupyter Notebook to Production API in 30 Minutes (FastAPI + Render)
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From Jupyter Notebook to Production API in 30 Minutes (FastAPI + Render)
Stop keeping your models in notebooks. Deploy them as real APIs that anyone can call. Continue reading on Medium »
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From Jupyter Notebook to Production API in 30 Minutes (FastAPI + Render)
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From Jupyter Notebook to Production API in 30 Minutes (FastAPI + Render)
Stop keeping your models in notebooks. Deploy them as real APIs that anyone can call. Continue reading on Medium »
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Chapter 10: Multi-Head Attention and the MLP Block
Dev.to · Gary Jackson 📐 ML Fundamentals ⚡ AI Lesson 2w 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.
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Human in the Loop: Using Confidence Scores to Build Reliable Document Extraction
Dev.to · Iteration Layer 📐 ML Fundamentals ⚡ AI Lesson 2w 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...
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Python for Data Science — Array Operations and Broadcasting
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Python for Data Science — Array Operations and Broadcasting
A practical guide to how NumPy performs math across arrays — including element-wise operations, shape compatibility, and broadcasting, one… Continue reading on
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My ML Model Returned HTTP 200 on Every Request. It Was Still Wrong.
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
My ML Model Returned HTTP 200 on Every Request. It Was Still Wrong.
How I learned that deploying a model and knowing it works are two very different things. Continue reading on Artificial Intelligence in Plain English »
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Gradient Descent from First Principles: Why Adam Outperforms SGD on Transformers
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Gradient Descent from First Principles: Why Adam Outperforms SGD on Transformers
Every transformer you have ever trained was optimised with Adam or AdamW. Most engineers who train them treat the optimizer as a black box… Continue reading on
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Dev.to AI 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Real-Time Fraud Detection in Java with Kafka Streams and Vector Similarity
This content is based on a talk that my colleague Tim Kelly and I presented at DevNexus 2026 in Atlanta, one of the largest Java conferences in the world. Imagi
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Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker
Dev.to · Tebogo Tseka 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker
Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker Every...
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The Real Skill Isn’t Python It’s Knowing the Right Libraries
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
The Real Skill Isn’t Python It’s Knowing the Right Libraries
Most people think they’re getting better at Python. Continue reading on Medium »
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Python Roadmap for Beginners in 2026: Skills That Actually Make You Job-Ready
Dev.to · Zestminds Academy 📐 ML Fundamentals ⚡ AI Lesson 2w 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...
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Radiomics in Medical Imaging: Unlocking Hidden Patterns for Early Disease Detection
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Radiomics in Medical Imaging: Unlocking Hidden Patterns for Early Disease Detection
Medical imaging has long been a cornerstone of disease diagnosis. From CT scans to MRI and ultrasound, clinicians rely heavily on visual… Continue reading on Me
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Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Generative AI From First Principles — Article 5 (Recurrent Neural Networks)
Recap: Why We Needed Something Beyond Basic Neural Networks Continue reading on Medium »
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Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Generative AI From First Principles — Article 5 (Recurrent Neural Networks)
Recap: Why We Needed Something Beyond Basic Neural Networks Continue reading on Medium »
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Why Data Quality is Becoming More Important Than Model Size in Modern AI Systems
Dev.to · Vishal Uttam Mane 📐 ML Fundamentals ⚡ AI Lesson 2w 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...
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8 Powerful Python Tricks Every Developer Should Know (Save Time & Write Cleaner Code)
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
8 Powerful Python Tricks Every Developer Should Know (Save Time & Write Cleaner Code)
The automation-first Python techniques that quietly separate efficient developers from exhausted ones Continue reading on CodeToDeploy »
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Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From SGD to LAMB: A Deep Engineering Walkthrough of Modern Optimizers
Why we moved from “just gradient descent” to layer-wise trust ratios — and what each step actually fixes. Continue reading on Medium »
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Want to Learn ML? These 10 Questions Are the Perfect Start
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Want to Learn ML? These 10 Questions Are the Perfect Start
The fastest way to understand machine learning Continue reading on Activated Thinker »
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Want to Learn ML? These 10 Questions Are the Perfect Start
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Want to Learn ML? These 10 Questions Are the Perfect Start
The fastest way to understand machine learning Continue reading on Activated Thinker »
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Want to Learn ML? These 10 Questions Are the Perfect Start
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Want to Learn ML? These 10 Questions Are the Perfect Start
The fastest way to understand machine learning Continue reading on Activated Thinker »
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Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
What Is Regularization in Machine Learning? — L1, L2, Dropout, and How Models Learn to Generalize
A complete, practical guide to controlling model complexity, improving generalization, and choosing the right regularization strategy Continue reading on Medium
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Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
What Is Regularization in Machine Learning? — L1, L2, Dropout, and How Models Learn to Generalize
A complete, practical guide to controlling model complexity, improving generalization, and choosing the right regularization strategy Continue reading on Medium
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Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
How Deep Learning Actually Trains: Gradient Noise, Adam, and Learning Rate Scheduling Explained
From gradient noise to Adam and cosine decay, a complete deep dive into how modern optimization methods reduce instability, adapt… Continue reading on Medium »
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Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
How Deep Learning Actually Trains: Gradient Noise, Adam, and Learning Rate Scheduling Explained
From gradient noise to Adam and cosine decay, a complete deep dive into how modern optimization methods reduce instability, adapt… Continue reading on Medium »
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Dev.to AI 📐 ML Fundamentals ⚡ AI Lesson 2w ago
How Gap's AI Styling Tool Can Actually Upgrade Your Wardrobe
Gap Inc. AI-powered styling recommendations are machine learning-driven outfit suggestions generated by analyzing a user's stated preferences, purchase history,
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Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
Medium · AI 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
From the continuous world of signal processing through discrete pixel arithmetic to bare-metal GPU silicon — the convolution operation… Continue reading on Data
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Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
From the continuous world of signal processing through discrete pixel arithmetic to bare-metal GPU silicon — the convolution operation… Continue reading on Data
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Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
From the continuous world of signal processing through discrete pixel arithmetic to bare-metal GPU silicon — the convolution operation… Continue reading on Data
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Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Inside the Convolution Operation: From Mathematical First Principles to Neural Architecture
From the continuous world of signal processing through discrete pixel arithmetic to bare-metal GPU silicon — the convolution operation… Continue reading on Data
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Building Samaritan: A Multi-Camera Real-Time Face Recognition System in Python — Part 2
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Building Samaritan: A Multi-Camera Real-Time Face Recognition System in Python — Part 2
Build real-time face recognition in Python with OpenCV, DeepFace, ArcFace embeddings, and live webcam-based identity matching. Continue reading on Medium »
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Building Vector Search? Why FAISS Alone Isn’t Enough
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Building Vector Search? Why FAISS Alone Isn’t Enough
What FAISS Does Well, Where It Stops, and When to Use a Vector Database Instead Continue reading on Towards AI »
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Building Vector Search? Why FAISS Alone Isn’t Enough
Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Building Vector Search? Why FAISS Alone Isn’t Enough
What FAISS Does Well, Where It Stops, and When to Use a Vector Database Instead Continue reading on Towards AI »
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From Clustering to Forecasting: A Full-Season Data-Driven Look at the 2023 F1 Season
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
From Clustering to Forecasting: A Full-Season Data-Driven Look at the 2023 F1 Season
Decoding driving style and lap-time prediction across all 22 races, 22 drivers, and 21,279 laps. Continue reading on Medium »
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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 2w 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...
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Building a Real‑Time Anomaly Detection Engine for Web Traffic
Dev.to · Patrick Onwujekwe 📐 ML Fundamentals ⚡ AI Lesson 2w 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...
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How I Built a Real-Time HTTP Traffic Anomaly Detector for a Cloud Storage Platform
Dev.to · Adjerese Precious 📐 ML Fundamentals ⚡ AI Lesson 2w 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...
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Crack a Classic DSA Interview Question: Reverse a Linked List in Python!
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Crack a Classic DSA Interview Question: Reverse a Linked List in Python!
If you’re preparing for coding interviews, this one comes up a lot: reversing a singly linked list. It’s a fundamental problem that tests… Continue reading on M
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Real-Time Anomaly Detection Engine for a Cloud Storage Platform
Dev.to · Timilehin Obalereko 📐 ML Fundamentals ⚡ AI Lesson 2w 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...
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We Built Something That Didn’t Exist. Today, We’re Sharing It With the World.
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
We Built Something That Didn’t Exist. Today, We’re Sharing It With the World.
By Andrew Moore, CEO & Co-Founder, Lovelace Continue reading on Medium »
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My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem
I trained an ML model to predict employee skill gaps and honestly the first time it ran I expected something rough. Beginner project… Continue reading on Medium
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My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem
I trained an ML model to predict employee skill gaps and honestly the first time it ran I expected something rough. Beginner project… Continue reading on Medium
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My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 2w ago
My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem
I trained an ML model to predict employee skill gaps and honestly the first time it ran I expected something rough. Beginner project… Continue reading on Medium
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Quantunix AI | L’intelligence artificielle peut-elle vraiment prédire les marchés crypto ?
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Quantunix AI | L’intelligence artificielle peut-elle vraiment prédire les marchés crypto ?
Algorithmes performants d’un côté, limites structurelles bien réelles de l’autre. Continue reading on Medium »
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From Pixels to Prescriptions: Engineering OCR Pipelines for Medical Report Simplification Using MongoDB
Dev.to · Kotha Deepak Reddy 📐 ML Fundamentals ⚡ AI Lesson 2w 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 |...
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The Sous Chef Who Guesses in Batches
Dev.to · Bongho Tae 📐 ML Fundamentals ⚡ AI Lesson 2w ago
The Sous Chef Who Guesses in Batches
When Waiting Becomes the Problem You are sitting in a restaurant, watching the kitchen...
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