#SGD
15 related articles
·5 min
30+ Free AI & Machine Learning Classic Textbooks: All in One GitHub Repository
Awesome Free AI Books is an open-source repo with 30+ legally free AI & ML classic textbooks covering deep learning, reinforcement learning, NLP, LLMs, and more — all linking to official sources with weekly automated link checks.
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·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.
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·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.
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·4 min
AI Text Detection and Privacy Protection: The Dual Dilemma of LLM Security
How does watermarking work — and why won't companies deploy it? How does differential privacy defend against membership inference attacks? Based on talks by IISc and IIT scholars, this article unpacks the core mechanisms and real challenges in LLM security.
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·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.
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·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.
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·2 min
Run Python in Your Browser! Why This Free Machine Learning Course Is Worth a Try
ai.coredump.digital is a completely free, no-signup, from-scratch machine learning course that runs Python directly in your browser, covering 11 ordered learning tracks with 970 quiz questions and an interview drill mode.
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The Theory of Deep Learning: Why Do Ne…
·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.
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·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.
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·3 min
Inside the ML Engineer Coding Assessment at Top US Tech Companies: What's Tested and How to Prepare
A firsthand account shared on Reddit reveals what a machine learning engineer online assessment (OA) at a top US tech company is really like. This article breaks down OA modules, role differences, and prep strategies for FAANG job seekers.
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·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.
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·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.
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·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.
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·3 min
Fired 6 Times, He Achieved 95% Profit Margins with Digital Products — Here's How
Indie developer Marc Lou was fired 6 times before pivoting to digital products, achieving $1.5M+ annual revenue with 95% profit margins. Learn his business model, pricing strategy, and AI marketing tactics.
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Tutorials·3 min
Efficient PyTorch Learning: A Source Code-Driven Methodology
A proven PyTorch learning method: spend 2-3 days on basics, then advance rapidly by reading U-Net and ViT source code line by line. Master PyTorch through source code-driven learning.
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