#supervised vs unsupervised learning
19 related articles
·3 min
PIRL Framework Explained: A Smooth Transition from Open-Loop Exploration to Closed-Loop Reinforcement Learning
Deep dive into the PIRL reinforcement learning framework: how to smoothly transition from open-loop exploration to closed-loop RL, mitigating the exploration-exploitation dilemma and improving sample efficiency.
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·3 min
Replacing spaCy's Sentencizer with yasbd: Accuracy Jumps from 55% to 98.9%
spaCy's default Sentencizer achieves only 55.4% accuracy on edge cases, while open-source library yasbd reaches 98.9%. Analysis of limitations and integration code examples.
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Kronos Financial Foundation Model: Usi…
·7 min
Kronos Financial Foundation Model: Using AI to Read the Market Language of Candlesticks
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.
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·2 min
GPT-5.6 Soul In-Depth Review: Can Multi-Agent Programming Mode Reshape the AI Development Tools Landscape?
GPT-5.6 Soul review: Super Mode hits 91.9% on TerminalBench. We break down multi-agent scheduling, benchmark controversies, and real-world dev tool comparisons.
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·4 min
From Tokens to Agents: A Complete Guide to Core AI Concepts
Master 8 core AI concepts — LLM, Token, Context Window, Prompt, Tool, Agent, MCP, and Agent Skill — and understand the complete logic chain behind AI's evolution.
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·4 min
AI Large Language Models for Beginners: Core Concepts, History, and China's Model Landscape
A beginner's guide to AI large models: clarify the relationships between AI, ML, deep learning, and LLMs, trace the journey from Deep Blue to ChatGPT and DeepSeek, and explore China's model landscape.
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·2 min
Deep Dive into MCP: How Large Language Models Connect to the Outside World
MCP (Model Context Protocol) is the standardized protocol connecting AI models to external tools and data — the 'USB-C port' of the AI era. Learn its origins and value.
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·2 min
Learning MLOps from Scratch: A Deep Dive into the 24-Month Roadmap
Can you learn MLOps from scratch? This guide breaks down core skill requirements and offers a practical 4-phase, 24-month roadmap covering Python, ML, DevOps, and MLflow.
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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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·5 min
The Five Unsolved Challenges of Production MLOps: A Deep Dive into GPU Scheduling, Fault-Tolerant Training, and Model Observability
A deep dive into the five genuinely tough challenges of production MLOps: fault-tolerant training on Spot instances, cross-team GPU scheduling, data reproducibility, model observability, and inference cost optimization.
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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
K-means vs Random Forest: How to Choose the Right Algorithm from First Principles
A machine learning exam question pitting K-means against Random Forest sparks debate. Learn the core difference between supervised and unsupervised learning, and how to choose the right algorithm for mixed-feature tasks.
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·4 min
Grok vs GPT vs Claude: Which AI is the Best Coding Assistant?
Grok 4.5, GPT-5.5, and Claude go head-to-head on the same coding tasks. A deep comparison of code quality, UI design, and engineering standards to help you choose the right AI coding assistant.
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·7 min
Agentic AI Reshapes Genomic Interpretation: A SOTA Case Study in Interstitial Lung Disease Diagnosis
How Agentic AI achieves SOTA performance in interstitial lung disease (ILD) genomic interpretation through autonomous planning, multi-step reasoning, and tool calling—and its clinical impact.
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·3 min
Loop Engineering: The New Paradigm for AI Self-Evolution
Loop Engineering by Anthropic is a new AI paradigm using four components—Mutator, Executor, Evaluator, Selector—to build self-iterating closed loops. Learn the architecture, use cases, and how to get started.
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·7 min
GPT Account Sharing Traps: The Hidden Risks Behind Low-Cost Shared Subscriptions
Is GPT Pro carpooling or account top-up really reliable? This article analyzes the risks of low-cost sharing including account security, privacy leaks, financial loss, and compliance issues.
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Deep Dives·4 min
Introduction to Artificial Intelligence: A Comprehensive Guide from Basic Concepts to Core Machine Learning Principles
A comprehensive guide to AI definitions, working principles, strong vs. weak AI, and the relationship between machine learning and deep learning. Perfect for beginners entering the AI field.
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Deep Dives·3 min
NVIDIA Fleet Intelligence Explained: Real-Time Monitoring and Intelligent Optimization for GPU Clusters
Deep dive into NVIDIA Fleet Intelligence for GPU clusters: real-time visualization, AI anomaly detection, utilization optimization, and energy management to boost large-scale GPU infrastructure efficiency.
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