#adaptive moment estimation

19 related articles

May 25, 2026·1 min

Deep Dive into the Adam Optimizer: A Complete Guide to Adaptive Moment Estimation

Deep breakdown of Adam optimizer's three core steps: first moment for gradient momentum, second moment for adaptive learning rates, and bias-corrected parameter updates.

Aug 10, 2026·5 min

Building a Neural Network from Scratch with NumPy: A Complete Hands-On Guide

Learn how to build a neural network from scratch using only Python and NumPy, covering forward propagation, backpropagation, gradient descent with full code walkthrough and learning resources.

Aug 9, 2026·3 min

Master AI Math with 5 Equations: 20 Free Python Hands-On Projects

A free ML workbook distills core machine learning math into 5 equations with 20 runnable Python projects covering gradient descent, backpropagation, loss functions, and more across NumPy, PyTorch, and XGBoost.

Aug 6, 2026·3 min

Building AI from Scratch: A Complete Learning Path with 503 Lessons in This Open-Source Course

AI Engineering from Scratch is an open-source course with 503 lessons across 20 phases, from linear algebra to autonomous agents, emphasizing hand-implementation before frameworks, supporting Python/TypeScript/Rust/Julia, with 46K+ GitHub stars.

Aug 5, 2026·3 min

Weak at ML Programming? A Complete Path to Breakthrough Through Systematic Practice

A systematic coding practice path for ML practitioners who 'understand theory but can't implement,' covering math basics to deep learning components with Deep-ML platform guidance.

Aug 5, 2026·3 min

Fine-Tuning 8B LLMs on a 4GB GPU: A Deep Dive into VRAM Optimization Techniques

Learn how to fine-tune 8B parameter LLMs on a 4GB laptop GPU using QLoRA quantization, gradient checkpointing, and gradient accumulation VRAM optimization techniques.

Jul 31, 2026·4 min

Poth Labs: Reshaping Enterprise Customer Knowledge Management with Relationship Networks

Poth Labs models customer knowledge as a dynamic relationship network, using cross-source reasoning and adaptive surveys to help enterprises understand churn and feature adoption.

Jul 30, 2026·3 min

Reconstructing Meshes from a Single Normal Map: A Weighted Approach to the Depth Discontinuity Problem

Exploring the core challenge of reconstructing 3D meshes from normal maps—handling depth discontinuities. Learn how per-pixel weights enable natural surface breaks and examine unresolved issues in fine structure reliability and absolute scale calibration.

Jul 30, 2026·3 min

Reconstructing Meshes from a Single Normal Map: A Per-Pixel Weight Approach to Depth Discontinuities

Exploring depth discontinuity handling in 3D mesh reconstruction from normal maps. Learn how per-pixel weights let surfaces naturally break apart, avoiding geometric errors from forced integration.

Jul 29, 2026·2 min

Stop Blindly Using Adam: The Hidden Pitfalls and Practical Boundaries of Optimizers

Deep analysis of Adam optimizer failure mechanisms in RL and deep Transformer training, revealing the mathematical roots of loss burstiness from second moment estimation, with practical solutions.

Jul 28, 2026·4 min

Statistical Foundations of Machine Learning: From MLE to EWMA and Deep Learning Optimizer Principles

Deep dive into core ML statistics: MLE derivations, multivariate Gaussian, linear regression and least squares equivalence, empirical risk minimization, method of moments, and how EWMA connects to Adam optimizer.

Jul 28, 2026·4 min

Statistical Foundations of Machine Learning: From MLE to EWMA and Deep Learning Optimizer Principles

Deep analysis of core ML statistics concepts covering MLE derivation, multivariate Gaussian, linear regression and least squares equivalence, empirical risk minimization, method of moments, and EWMA's connection to Adam optimizer.

Jul 24, 2026·7 min

DeepSeek's Mysteriously Retracted Paper: Breaking AI's Multimodal Reasoning Bottleneck with Visual Primitives

DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.

Jul 13, 2026·3 min

How to Systematically Learn Machine Learning After Mastering Math and Python

Already know math and Python? Learn the complete machine learning roadmap: from data science tools and classical algorithms to deep learning frameworks and specialization.

Jul 13, 2026·3 min

Math Foundations for Machine Learning: A Roadmap Covering Four Core Areas with Recommended Resources

Struggling with math for ML? This guide covers linear algebra, calculus, probability, and optimization with top resources like 3Blue1Brown and Mathematics for Machine Learning.

Jul 12, 2026·7 min

Training Machine Learning Models from Scratch: A Complete Beginner's Guide

A complete walkthrough of training machine learning models from scratch—covering problem definition, data preprocessing, algorithm selection, hyperparameter tuning, and evaluation, with tool recommendations for beginners.

Jul 11, 2026·5 min

Essential Math for AI/ML Research: Should Undergrads Choose the Specialist Sequence or the Major Track?

Aiming for AI/ML research? How should you pick undergrad math courses? This article breaks down linear algebra, probability & statistics, and optimization, weighing the specialist sequence vs. the Major track.

Jul 11, 2026·3 min

Reward Shaping and RL Debugging in Practice: A Systematic Guide from Oscillation to Convergence

Using an FPV drone RL project as a case study, this guide covers reward shaping principles, Bang-Bang control hacking, module isolation, single-variable debugging, and behavior visualization to solve common RL training issues.

Jul 10, 2026·7 min

Complete Guide to Training Small Models on Kaggle's Free Compute: GPU Quotas and Practical Tips

In-depth guide to Kaggle's free-tier compute: P100/T4 GPU with 30 hours/week quota, 12-hour sessions, suitable models like CNN and BERT fine-tuning, plus tips like mixed precision and checkpointing to start deep learning at zero cost.