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

Showing 1,220 reads from curated sources

55. Multiple Regression: More Features, More Power (And More Ways to Break Things)
Dev.to · Akhilesh 📐 ML Fundamentals ⚡ AI Lesson 1w 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...
How to Convert Text to Binary (Beginner-Friendly Guide with Examples)
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 1w ago
How to Convert Text to Binary (Beginner-Friendly Guide with Examples)
Computers don’t understand text the way humans do. Instead, they rely on binary code — a system made up of only two digits: 0 and 1. Continue reading on Medium
Beyond the Empty Dock: How We Used Machine Learning to Optimize Washington D.C.’s Urban Mobility
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Beyond the Empty Dock: How We Used Machine Learning to Optimize Washington D.C.’s Urban Mobility
The Invisible Heartbeat of Washington, D.C. Continue reading on Medium »
DBSCAN Explained: Clustering Data by Density, Not Distance
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
DBSCAN Explained: Clustering Data by Density, Not Distance
Welcome to another post in my ongoing machine learning adventure. This blog is part of a series where I’m diving into the world of ML —… Continue reading on Med
DBSCAN Explained: Clustering Data by Density, Not Distance
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 1w ago
DBSCAN Explained: Clustering Data by Density, Not Distance
Welcome to another post in my ongoing machine learning adventure. This blog is part of a series where I’m diving into the world of ML —… Continue reading on Med
Medium · LLM 📐 ML Fundamentals ⚡ AI Lesson 1w ago
When Your Model Doesn’t Learn: The Power of Learning Rate
A model is training. The code runs fine. The data looks good. The architecture is solid. Continue reading on Medium »
Ilya’s List, Part 17: Relational Recurrent Neural Networks: What If an RNN Had More Than One…
Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Ilya’s List, Part 17: Relational Recurrent Neural Networks: What If an RNN Had More Than One…
A normal recurrent neural network is pretty easy to describe. Continue reading on Medium »
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Building a molecular binding affinity prediction pipeline with Graph Attention Networks, MC Dropout uncertainty quantification, and… Continue reading on Medium
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 1w ago
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Building a molecular binding affinity prediction pipeline with Graph Attention Networks, MC Dropout uncertainty quantification, and… Continue reading on Medium
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 1w ago
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Building a molecular binding affinity prediction pipeline with Graph Attention Networks, MC Dropout uncertainty quantification, and… Continue reading on Medium
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
Building a molecular binding affinity prediction pipeline with Graph Attention Networks, MC Dropout uncertainty quantification, and… Continue reading on Medium
Building a Semantic Search Engine for Patent Prior Art Discovery Using SBERT and FAISS
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Building a Semantic Search Engine for Patent Prior Art Discovery Using SBERT and FAISS
Building a Semantic Search Engine for Patent Prior Art Discovery Using SBERT and FAISS Continue reading on Medium »
Automation, Security, and Precision Medicine Innovations of Modern Deep Learning
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Automation, Security, and Precision Medicine Innovations of Modern Deep Learning
Deep learning (DL) has moved beyond manual configuration toward automated, high-performance systems. Continue reading on Quant Review »
pandas.get_dummies() Metodu Nedir, Makine Öğrenmesinde Nasıl Kullanılır
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
pandas.get_dummies() Metodu Nedir, Makine Öğrenmesinde Nasıl Kullanılır
Veri setlerinde karşımıza çıkan kategorik verileri nominal (sırasız) ve ordinal (sıralı) olarak ikiye ayırabiliriz. Ordinal veriler… Continue reading on Medium
Dev.to AI 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Arrays & Hashing: A Beginner’s Guide to the Most Important Pattern in DSA
Arrays & Hashing: A Beginner's Guide to the Most Important Pattern in DSA If you are just starting out with Data Structures and Algorithms, Arrays & Has
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Arrays & Hashing: A Beginner’s Guide to the Most Important Pattern in DSA
Arrays & Hashing: A Beginner’s Guide to the Most Important Pattern in DSA Continue reading on Medium »
Solving the ‘8-puzzle’ using a basic AI algorithm
Medium · AI 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Solving the ‘8-puzzle’ using a basic AI algorithm
რას გულისხმობს “8-puzzle” პრობლემა. Continue reading on Medium »
Solving the ‘8-puzzle’ using a basic AI algorithm
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Solving the ‘8-puzzle’ using a basic AI algorithm
რას გულისხმობს “8-puzzle” პრობლემა. Continue reading on Medium »
Solving the ‘8-puzzle’ using a basic AI algorithm
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Solving the ‘8-puzzle’ using a basic AI algorithm
რას გულისხმობს “8-puzzle” პრობლემა. Continue reading on Medium »
Solving the ‘8-puzzle’ using a basic AI algorithm
Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Solving the ‘8-puzzle’ using a basic AI algorithm
რას გულისხმობს “8-puzzle” პრობლემა. Continue reading on Medium »
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Medium · AI 📐 ML Fundamentals ⚡ AI Lesson 1w ago
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In the world of production AI, accuracy alone is not enough.. Continue reading on Medium »
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Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 1w ago
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In the world of production AI, accuracy alone is not enough.. Continue reading on Medium »
Every AI Training Pipeline Has a Ceiling Problem
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Every AI Training Pipeline Has a Ceiling Problem
How SFT, RL, and distillation shape what your model can and can’t learn. Continue reading on Medium »
You Don’t Have a Memory Problem
Medium · Deep Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
You Don’t Have a Memory Problem
You have an understanding problem. And the difference explains almost everything about why some knowledge sticks and some disappears. Continue reading on Medium
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
How does a machine know which two things are similar? Continue reading on Medium »
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
How does a machine know which two things are similar? Continue reading on Medium »
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
How does a machine know which two things are similar? Continue reading on Medium »
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Distance Metrics: Euclidean, Manhattan & Cosine Similarity
How does a machine know which two things are similar? Continue reading on Medium »
Detecting Fraud in Digital Transactions: A Machine Learning Approach Using Logistic Regression —…
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Detecting Fraud in Digital Transactions: A Machine Learning Approach Using Logistic Regression —…
Introduction In the era of digital banking and cashless transactions, credit cards have become one of the most widely used payment methods… Continue reading on
Detecting Fraud in Digital Transactions: A Machine Learning Approach Using Logistic Regression —…
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Detecting Fraud in Digital Transactions: A Machine Learning Approach Using Logistic Regression —…
Introduction In the era of digital banking and cashless transactions, credit cards have become one of the most widely used payment methods… Continue reading on
JAX Gotchas That Confused Me (And Will Confuse You Too)
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
JAX Gotchas That Confused Me (And Will Confuse You Too)
So I’ve been learning JAX for a while now. And honestly? It’s amazing. But it also made me go “wait… what??” more times than I’d like to… Continue reading on Me
6 Programming Rules I Broke Before Improving
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
6 Programming Rules I Broke Before Improving
The habits that quietly slowed my growth Continue reading on Level Up Coding »
6 Programming Rules I Broke Before Improving
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
6 Programming Rules I Broke Before Improving
The habits that quietly slowed my growth Continue reading on Level Up Coding »
4 Python Bugs That Taught Me More Than Tutorials
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
4 Python Bugs That Taught Me More Than Tutorials
The bugs that made my Python automation smarter. Continue reading on Stackademic »
4 Python Bugs That Taught Me More Than Tutorials
Medium · Programming 📐 ML Fundamentals ⚡ AI Lesson 2w ago
4 Python Bugs That Taught Me More Than Tutorials
The bugs that made my Python automation smarter. Continue reading on Stackademic »
ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
arXiv:2604.26999v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embed
ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Binary Spiking Neural Networks as Causal Models
arXiv:2604.27007v1 Announce Type: new Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally de
ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs
arXiv:2604.27126v1 Announce Type: new Abstract: This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Ba
ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Machine Collective Intelligence for Explainable Scientific Discovery
arXiv:2604.27297v1 Announce Type: new Abstract: Deriving governing equations from empirical observations is a longstanding challenge in science. Although artifi
ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Measurement Risk in Supervised Financial NLP: Rubric and Metric Sensitivity on JF-ICR
arXiv:2604.27374v1 Announce Type: new Abstract: As LLMs become credible readers of earnings calls, investor-relations Q\&A, guidance, and disclosure language, s
ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach
arXiv:2604.27387v1 Announce Type: new Abstract: Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world sys
Batch Normalization
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Batch Normalization
The trick that makes deep learning actually work. Continue reading on Medium »
Implementing a User-Based Recommendation System from Scratch in Python
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Implementing a User-Based Recommendation System from Scratch in Python
What is User-Based Collaborative Filtering? Continue reading on Medium »
Implementing a User-Based Recommendation System from Scratch in Python
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Implementing a User-Based Recommendation System from Scratch in Python
What is User-Based Collaborative Filtering? Continue reading on Medium »
Implementing a User-Based Recommendation System from Scratch in Python
Medium · Python 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Implementing a User-Based Recommendation System from Scratch in Python
What is User-Based Collaborative Filtering? Continue reading on Medium »
When Your Model Cheats Without Cheating: A Lesson in What “Source Separation” Really Protects You…
Medium · LLM 📐 ML Fundamentals ⚡ AI Lesson 2w ago
When Your Model Cheats Without Cheating: A Lesson in What “Source Separation” Really Protects You…
What I learned building a political bias classifier — and why the most interesting result wasn’t the best one. Continue reading on Medium »
Our Fraud Detection Model Had 90% False Negatives. Here Is How We Fixed It.
Medium · Machine Learning 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Our Fraud Detection Model Had 90% False Negatives. Here Is How We Fixed It.
A technical deep dive into AutoEncoder anomaly scoring, Gradient Boosting ensembles, SHAP explainability, and real-time Kafka streaming. Continue reading on Med
Our Fraud Detection Model Had 90% False Negatives. Here Is How We Fixed It.
Medium · Data Science 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Our Fraud Detection Model Had 90% False Negatives. Here Is How We Fixed It.
A technical deep dive into AutoEncoder anomaly scoring, Gradient Boosting ensembles, SHAP explainability, and real-time Kafka streaming. Continue reading on Med