Essential AI Concept - Category - DataSci Ocean

Essential AI Concept

2026

vLLM's 106x Cost Claim: The Real Engineering of Agentic Serving 09-24

TypeSafe AI's Jev: 193.6x Claim vs. the ~25x Reality 09-22

Claude Code Concepts: Agentic Loop, Tools, Skills 07-24

Dropout Explained: The Simple Secret to Preventing Neural Network Overfitting 07-20

Understanding Backpropagation: How Gradients Are Calculated 07-15

An Introduction to Stochastic Gradient Descent 07-09

Deep Learning Fundamentals: An Introduction to Gradient Descent 06-28

Is a Vector 1D or 5D? The Two Meanings of Dimension 06-23

Deep Learning Fundamentals: The MNIST Dataset and the Cost Function 06-20

Machine Learning Basics: The Bias-Variance Tradeoff 06-16

Why a Digit Classifier Needs 10 Output Neurons, Not 4 06-14

Deep Learning, Step One: Neural Network Terminology 06-11

An Improved Perceptron: Understanding the Sigmoid Neuron 06-09

Beyond Text-to-SQL: Implementing OpenAI’s 6-Layer Context Data Agent and the Hidden Challenges 04-25

Harness Engineering: Building Self-Evolving AI Agents with Ralph Loop and MemRL 04-05

Takeaways of vLLM Semantic Router 03-29

Beyond the Context Window: A Deep Dive into Context Engineering for AI Agents 03-22

Beyond Prompt Engineering: The System Engineering of AI Agents — Deconstructing Memory, Tool-Making, and Autonomy in OpenClaw 03-16

2024

The 3 Stages of LLM Training: A Deep Dive into Reinforcement Learning from Human Feedback (RLHF) 02-27

2023

From BERTology to PEFT: Notes on an AACL 2022 Tutorial 09-01

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