#stochastic gradient descent
14 related articles
·2 min
Gradient-Free Evolutionary Model GENREG: How Radial Space Search Bypasses Backpropagation
GENREG-Radial Space is a gradient-free evolutionary optimization model that replaces backpropagation with structured radial space search. This article analyzes its core mechanisms, temporal evolution design, exploration-exploitation balance, and potential as a hybrid paradigm.
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From Math to AI Research Engineer: A D…
·2 min
From Math to AI Research Engineer: A Deep Dive into the Viral GitHub Learning Roadmap
A GitHub project called maths-cs-ai-compendium surpassed 6,000 Stars with a roadmap for becoming an AI/ML Research Engineer. Here's what makes it worth following.
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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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·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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·6 min
AI Agent Security in Practice: 7 Attack Techniques and a Five-Layer Defense System
An in-depth breakdown of the 7 major attack techniques against AI agents (prompt injection, data poisoning, image attacks, etc.) and a five-layer defense system, with real cases from Doubao and DeepSeek.
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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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·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.
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·7 min
Dispersion Loss: A Key Technique for Solving Embedding Condensation in Small Language Models
Embedding condensation is a hidden bottleneck in small language model training. Dispersion Loss combats this by enforcing representation spread during training at zero inference cost.
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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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Tutorials·2 min
Deep Learning Beginner Tutorial: From Neural Network Fundamentals to Image Recognition in Three Days
A systematic review of a three-day deep learning crash course covering neural network math, gradient descent, backpropagation, TensorFlow, CNNs, and transfer learning with practical tips.
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