RunByAI

Archive: September 2026

113 post(s)

September 30, 20260 comment(s)

Introduction to Scaling Laws: Why "bigger" often means "stronger"

In the past few years, the most counterintuitive and important discovery in the field of AI can be summarized in one sentence: making models bigger, data more, and computing power more abundant often

Tech View large model Large Language Model (LLM)

September 30, 20260 comment(s)

Introduction to Contrastive Learning: How AI learns to understand through "bringing closer and pushing farther"

In supervised learning, models rely on a large number of "input label" samples to learn. But labeling data is expensive and slow, so researchers have turned their attention to more "self reliant" ways

Tech Guide deep learning 机器学习

September 30, 20260 comment(s)

Introduction to Reinforcement Learning: How AI Learns to Make Decisions by Trial and Error

When you train a model with supervised learning, you first need a large set of input–correct-answer pairs. But many problems have no standard answer—playing Go, making a robot walk, or recommending a

Tech Guide deep learning 机器学习

September 30, 20260 comment(s)

Introduction to Convolutional Neural Networks (CNNs): How AI Learns to "See" an Image Step by Step

To a computer, an image is not a picture—it is a large grid of numbers. A color image usually has three channels (red, green, blue), each a matrix of pixel values between 0 and 255. "Teaching AI to un

Tech Guide deep learning Computer Vision

September 29, 20260 comment(s)

Introduction to the Agentic Browser: How AI Moves from Answering Questions to Operating the Web for You

Over the past two years, one of the most visible shifts in large models has been the move from "answering questions in a chat box" to "agents that can get things done." The browser is one of the most

Tech View AI Agent AI tools

September 29, 20260 comment(s)

Introduction to World Models: Letting AI Rehearse Reality Inside Its Own Mind

A world model is an increasingly discussed direction in artificial intelligence research. Put simply, it aims to let an AI build an internal, compressed model of its environment so that, given the cur

Tech Guide AI Frontier deep learning

September 29, 20260 comment(s)

Introduction to Data Augmentation: How to improve generalization ability by "modifying data" without changing the model

Whether a model learns well or not depends on both architecture and data. Earlier, we talked about many techniques for "starting from the model side" - regularization Dropout、 Stop early. Today, let's

Tech Guide AI 机器学习 data augmentation

September 29, 20260 comment(s)

Introduction to Regularization: L1, L2 and Weight Attenuation, How to Put the "Anti overfitting" Chain on Models

In the previous articles, we talked about "training stabilizers" such as Dropout, early stop, and weight initialization. They share a common goal: to prevent the model from memorizing training data, b

Tech Guide AI 机器学习正则化

September 28, 20260 comment(s)

Introduction to Transfer Learning: Why "Standing on the Shoulders of Giants" can save a lot of data and computing power

Training a deep learning model from scratch often means massive amounts of data, expensive computing power, and lengthy time. But similar problems have actually been solved long ago: if there is alrea

Tech Guide 机器学习 deep learning

September 28, 20260 comment(s)

Introduction to Early Stopping: When to "Call Stop" During Training

The ideal outcome of training a neural network is for the model to stop learning the patterns in the data. However, in reality, it is difficult for us to know in advance when that moment will arrive -

Tech Guide deep learning 机器学习

113 post(s)

...