#algorithm derivation
88 related articles
Tutorials·2 min
Python Machine Learning in Practice: A Complete Guide from Algorithm Derivation to Code Implementation
A systematic Python ML course using a 3-stage framework: algorithm derivation, code implementation, and experimental analysis. Covers logistic regression, decision trees, and ensemble learning.
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·2 min
AI Cracks a 35-Year-Old Math Problem: Discovering an Unexpected New Term
The Theo Conjecture, unsolved for 35 years, has been cracked with an unexpected new term discovered. Exploring AI's evolving role in pure math research.
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·3 min
Preparation Guide for NLP Research Scientist Internships: Key Focus Areas and Common Pitfalls
A comprehensive guide to preparing for NLP Research Scientist Intern roles, covering evaluation criteria, foundational knowledge, paper reading strategies, hands-on skills, and common pitfalls.
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·3 min
Kimi Delta Attention Explained: The Evolution from Standard Attention to Linear Attention
A deep dive into Kimi Delta Attention (KDA): tracing the evolution from quadratic Softmax attention through linear attention, Delta rules, and gated decay mechanisms, with insights on associative memory and hardware optimization.
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·3 min
Kimi Delta Attention Explained: The Evolution from Standard Attention to Linear Attention
Deep dive into Kimi Delta Attention (KDA): from standard Softmax attention's quadratic bottleneck through linear attention, Delta Rule, and gated decay mechanisms — the complete evolution explained.
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·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.
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·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.
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·3 min
Learning AI Large Language Models: A Complete Roadmap from Beginner to Practitioner
A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.
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·3 min
Backend Developer Transition to AI/LLM: 4 Core Skills and a Practical Learning Roadmap
Complete guide for backend developers transitioning to AI/LLM engineering. Covers the 4 core skills—Python, RAG, Fine-tuning, and Agents—with a phased learning roadmap and practical project advice.
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·3 min
Three Essential Hard Skills Traditional Product Managers Need to Transition into AI PM
How can traditional product managers transition to AI PM? This article analyzes the essential differences and details three must-have skills: AI product cognition, advanced Prompt engineering, and large model technical logic.
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·3 min
Mathematical Foundations for Machine Learning: A Complete Learning Path from Linear Algebra to Probability Theory
A systematic guide to the three core math areas for ML—linear algebra, calculus, and probability—with verified free resources like Mathematics for Machine Learning, 3Blue1Brown, and practical learning strategies.
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·3 min
Low Initial Review Scores for NeurIPS 2026 Theory Papers? Community Discussion and In-Depth Analysis
NeurIPS 2026 theory papers are receiving low initial review scores. This article analyzes structural causes, scoring trends, and rebuttal strategies for theory researchers.
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·4 min
FPGA Softmax Approximation in Practice: A Detailed Guide to Taylor Expansion and Padé Approximation Methods
A detailed guide on efficiently implementing Softmax on FPGAs, comparing Taylor series and Padé approximation methods for accuracy and resource trade-offs, with Python simulation and range reduction techniques.
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·3 min
Vibe Coding for Beginners: Build Your First Project with AI from Scratch
What is Vibe Coding? Learn this new AI programming paradigm from scratch — no CS degree needed. Use Claude Code, Cursor, and more to build real projects by describing your ideas.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·6 min
The Boundaries of AI Coding: When Should You Show Yourself a Red Card?
As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.
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·3 min
The Three Core LLM Job Roles Explained: Entry Requirements and Career Roadmaps
A deep dive into the three core LLM job roles — Application Engineer, R&D Engineer, and Algorithm Engineer — covering academic requirements, salaries, and skill roadmaps.
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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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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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