#Adam optimizer

50 related articles

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 22, 2026·3 min

Qwen's Six-Generation Evolution: A Complete Breakdown from Architecture Innovations to the Reasoning Race

From Qwen1 to Qwen3-2507: a complete breakdown of six generations of evolution over two years — GQA, MoE, GRPO, dynamic thinking, and the core shift from architecture to reasoning.

Jul 20, 2026·3 min

Don't Start AI Learning with Machine Learning: A Beginner's Guide to the Onion Model

Gaurav Sen reveals the fatal trap in AI learning: starting from ML fundamentals often leads to burnout. Learn the Onion Model approach—RAG, Agents first, Transformers next, math last.

Jul 19, 2026·3 min

Gradient Descent and Backpropagation Explained: How Neural Networks Learn from Scratch

Understand how neural networks learn: a complete guide to cost functions, gradient descent, backpropagation, and SGD — ideal for deep learning beginners building intuition from the ground up.

Jul 19, 2026·2 min

Build From Scratch or Just Use sklearn? A Complete ML Engineer Learning Path Guide

Should you implement ML algorithms from scratch or just use sklearn? This guide breaks down the optimal learning path for ML engineers by career stage and company type.

JAX Host Offloading: A Practical Guide…

Jul 14, 2026·2 min

JAX Host Offloading: A Practical Guide to Breaking Through LLM Training Memory Bottlenecks

Memory capacity is the core bottleneck in LLM training. This guide explores JAX-based host offloading — covering optimizer state offloading, activation strategies, PCIe bandwidth trade-offs, and how it complements activation recomputation.

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 13, 2026·3 min

McNemar's Test for Comparing ML Models: Proper Usage Under Random Seeds

A deep dive into McNemar's test and its proper use in ML model comparison, covering how random seeds complicate experimental design and offering practical multi-seed strategies for statistically sound claims.

Jul 13, 2026·3 min

AI-Powered OSINT in Practice: A Learning Path and Tool Guide for CS Professionals

How can OSINT practitioners with a CS background automate intelligence with AI? This guide covers computer vision, VLMs, and Agent frameworks including YOLO, SAM, and Grounding DINO.

The Theory of Deep Learning: Why Do Ne…

Jul 12, 2026·4 min

The Theory of Deep Learning: Why Do Neural Networks Work? A Complete Overview of Theoretical Research

Deep learning shines in practice, but why does theory always lag behind? This article surveys the over-parameterization paradox, implicit regularization, NTK, the information bottleneck, and more.

Jul 12, 2026·7 min

Breaking Down the Core Skills of an AI Application Engineer: From Beginner to Industrial-Grade Deployment

Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.

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 10, 2026·4 min

DeepSeek-V4 Deep Dive: Million-Token Context Architecture Breakthrough and Core Innovations

Deep dive into DeepSeek-V4: 1.6T-parameter MoE, CSA+HCA hybrid attention, MHC & MUON optimizer. Inference FLOPs drop to 27% of V3.2, redefining open-source LLM SOTA.

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.

Jul 7, 2026·5 min

Nonuniform Tensor Parallelism: NVIDIA's New Fault-Tolerance Approach to Boost LLM Training Goodput

NVIDIA introduces Nonuniform Tensor Parallelism, letting GPUs bear different compute loads so training can continue without checkpoint rollback during hardware failures—boosting LLM training Goodput and fault tolerance at scale.

Jun 25, 2026·3 min

How Much Math Do AI Practitioners Really Need? A Deep Dive into Three Role Types

How much math do AI/ML practitioners really need? This article breaks down three roles — Users, Developers, and Researchers — and analyzes the math requirements for each to help you plan your learning path.

Jun 13, 2026·4 min

Neural Networks from Scratch: A Deep Dive into the Fundamentals from Linear Regression to Gradient Descent

From linear regression and logistic regression to gradient descent, this guide derives the core mechanisms of neural networks step by step, covering Sigmoid, cross-entropy, activation functions, and backpropagation.