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Introduction to Attention Mechanism: How Transformers "Focus on Key Points"

Why do we need attention mechanismsNot every word is equally important when dealing with a sentence or text. Traditional recurrent neural networks (RNNs) read in sequence one by one, relying on a "mem

Introduction to Regularization: How AI Models Prevent "rote memorization"

What is regularizationIn machine learning, overfitting is one of the most common and hidden problems: the model performs nearly perfectly on training data, and it is obviously inaccurate when encounte

Introduction to Dimensionality Reduction: How PCA and t-SNE Flatten High Dimensional Data

Why reduce dimensionsReal data often has dozens, hundreds, or even thousands of features: a small image has thousands of pixels, and a text vector may have hundreds of dimensions. As the dimensionalit

Introduction to Clustering: How Unsupervised Learning "Clusters Like Things"

What is clusteringClustering is a type of unsupervised learning method that does not have labels and only groups samples that look similar based on their similarity. It is different from classificatio

Tech Guide deep learning large model

Introduction to Activation Function: Why Neural Networks Cannot Do Without Nonlinear Switches

If there is only matrix multiplication, even the deepest neural network will only be a large linear transformation, and it will not have complex patterns at all. What truly brings neural networks to l

Tech Guide large model Reasoning optimization

Introduction to Decoding Strategies: How Big Models "Choose the Next Word"

When a big model completes a sentence, it faces tens of thousands or even tens of thousands of candidate words. Why does it pick out the next one? Why is the same question sometimes answered steadily

Tech Guide large model Reasoning optimization

Introduction to Speculative Decoding: How LLMs Speed Up Inference by Guessing and Verifying

Most people who have used local large models have a common experience: typing is too slow. Especially in dialogue based generation, the model must squeeze out each token one by one, making it difficul

October 5, 20260 comment(s)

Introduction to Explainable AI (XAI): Why AI Decisions Need to Be Explained

A large model rejected a loan application, but couldn't explain why; A medical imaging model marked the lesion, but the doctor didn't know what it was "seeing". As AI becomes increasingly involved in

Tech View 机器学习 AI Ethics

Introduction to Chain of Thought: Why Big Models Think Step by Step More Accurately

Why can the same big model sometimes provide quick and incorrect answers, but sometimes it can reliably deduce results? The difference often lies not in the model itself, but in 'how to ask'. The Chai

Tech Guide large model

October 5, 20260 comment(s)

Introduction to Few Shot Learning: How AI learns new tasks with "a few examples"

Traditional deep learning models typically require thousands of annotated samples to learn a task well. But humans are different: just by looking at two or three photos of unfamiliar animals, you can