#calculus for deep learning
71 related articles
·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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·3 min
How Hidden Layers Solve the XOR Problem: A Complete Walkthrough from Math to Geometry
A deep dive into how neural network hidden layers solve the XOR problem through feature space transformation, with math, geometry, and concrete examples.
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·2 min
Jensen Huang Champions Open-Source AI: Distillation Is Not Plagiarism, DeepSeek and Kimi Are Excellent
NVIDIA CEO Jensen Huang defends open-source AI, calls distillation legitimate learning, praises DeepSeek and Kimi, and co-signs open letter with 20+ companies while OpenAI and Google stay silent.
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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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·4 min
30+ Free Classic AI/ML Books Recommended: All Collected in One GitHub Repository
An open-source GitHub repo curates 30+ legally free AI/ML classic books covering deep learning, RL, NLP, computer vision & more, with automated link checking.
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·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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·2 min
From Zero to AI Career: A Complete Breakdown of a 3-Month Self-Study Roadmap
Want to break into AI from scratch? This article breaks down an efficient self-study roadmap: from Python, math, and machine learning basics to PyTorch, then to CV, NLP, and data mining—reaching entry-level career-switching intensity in 3 months.
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·2 min
How to Choose a Programming Language? A Rational Look at the Truth Behind Language Selection
Java, Python, Go, or a niche language? This article rationally analyzes programming language selection across three dimensions — probability, difficulty, and growth potential — to help you escape language-choice anxiety.
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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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·2 min
Revisiting SHRDLU: The Technical Lineage and Modern Value of Procedural Semantics
From SHRDLU to modern neuro-symbolic AI: explore procedural semantics, CCG grammars, semantic parsing, and interactive fiction engines in today's NLP landscape.
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·3 min
Math Learning Path for Machine Learning: From Resource Overload to Systematic Mastery
Overwhelmed by ML math courses? This guide maps out linear algebra, calculus, and probability into a practical learning path — from core courses to reference books.
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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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·2 min
Thinking Machines Releases Its First Open-Weight Model: Inkling
Thinking Machines Lab releases Inkling, its first open-weight model. Founded by former OpenAI members, the startup enters the LLM market with an open-weight strategy enabling local deployment and private fine-tuning.
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·2 min
Transitioning to AI/ML: Why Finding a Study Buddy Matters More Than Picking the Right Course
Transitioning from software dev to AI/ML is hard to do alone. Discover why finding a study buddy beats picking the perfect course — and how peer accountability solves the consistency, judgment-free questioning, and foundation-building challenges.
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·2 min
Symbolic AI and Neural Networks: A Systematic Learning Path and Core Mathematical Tools
A deep dive into symbolic vs. neural AI paradigms — exploring type theory, category theory, and algebraic geometry as mathematical bridges toward neuro-symbolic integration.
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Causal Theory Cracks Open the LLM Blac…
·2 min
Causal Theory Cracks Open the LLM Black Box: A New Paradigm for Mechanistic Interpretability
How can we solve the LLM black box problem? This article explores how causal theory powers mechanistic interpretability research — from causal intervention and activation patching to circuit discovery — and its implications for AI safety and alignment.
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Learning AI Without Math: 7 Mindset Sh…
·3 min
Learning AI Without Math: 7 Mindset Shifts That Make It Easier
Scared off by math? Learn 7 mindset shifts to understand AI without it — concepts first, analogies, hands-on practice, and layered understanding.
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After Getting Started with AI/ML: Shou…
·3 min
After Getting Started with AI/ML: Should You Pursue an Internship or Continue Studying?
Already trained models and implemented neural nets from scratch — should you apply for internships or keep studying? A practical guide to entry-level AI roles and how to advance.
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The Complete AI Researcher Learning Ro…
·3 min
The Complete AI Researcher Learning Roadmap: A Beginner's Guide to Deep Learning
A structured AI/ML learning roadmap covering Python, math, machine learning, deep learning, and MLOps — with timelines, milestones, and free resource recommendations.
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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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