Foundations
ML Fundamentals
Neural networks, backpropagation, gradient descent — the maths behind AI
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Showing 1,234 reads from curated sources
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
arXiv:2604.26644v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable o
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
A Randomized PDE Energy driven Iterative Framework for Efficient and Stable PDE Solutions
arXiv:2604.25943v1 Announce Type: cross Abstract: Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineerin
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction
arXiv:2604.25945v1 Announce Type: cross Abstract: Wireless radiance field (WRF) reconstruction aims to learn a continuous, queryable representation of radio fre
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
Mini-Batch Class Composition Bias in Link Prediction
arXiv:2604.25978v1 Announce Type: cross Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that t
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts
arXiv:2604.26024v1 Announce Type: cross Abstract: Class-level evaluation can conceal substantial performance disparities across subconcepts within the same clas
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts
arXiv:2604.26039v1 Announce Type: cross Abstract: The optimal kernel configuration for Mixture-of-Experts (MoE) inference depends on both batch size and the exp
ArXiv cs.AI
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
2w ago
Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization
arXiv:2604.26073v1 Announce Type: cross Abstract: Industrial chemical plants often operate under strict data confidentiality constraints, making centralized dat
Medium · LLM
📐 ML Fundamentals
⚡ AI Lesson
2w ago
Knowing When the Model Is Actually Right
The first thing I built at NovumAI wasn’t the model. It was the eval harness. Continue reading on Medium »
Medium · Machine Learning
📐 ML Fundamentals
⚡ AI Lesson
2w ago
proModeling+ 2026: The Full Refresh
A rebuilt pitch-quality stack with sharper predictiveness, faster stabilization, and a cleaner split between what a pitch is and what a… Continue reading on Med
Medium · Data Science
📐 ML Fundamentals
⚡ AI Lesson
2w ago
proModeling+ 2026: The Full Refresh
A rebuilt pitch-quality stack with sharper predictiveness, faster stabilization, and a cleaner split between what a pitch is and what a… Continue reading on Med
Medium · Machine Learning
📐 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 »
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 »
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.
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 »
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 »
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 »
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.
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...
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
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 »
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
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
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...
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 »
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...
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
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 »
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 »
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...
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 »
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 »
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 »
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 »
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 »
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
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
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 »
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 »
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,
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
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
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
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
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 »
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 »
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 »
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 »
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...
DeepCamp AI