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Understanding the Transformer Architecture

Introduction

Since its introduction in 2017, the Transformer architecture has become the cornerstone of natural language processing. This article provides an in-depth yet accessible explanation of Transformer’s core mechanisms.

Why Do We Need Transformers?

Before Transformers, RNNs and LSTMs were the mainstream methods for sequence modeling. However, they had several limitations:

1. Sequential Computation - Cannot be parallelized, leading to low training efficiency
2. Long-range Dependencies - Difficulty capturing long-distance contextual information
3. Gradient Issues - Long sequences prone to vanishing gradients

Transformers elegantly solve these problems through the self-attention mechanism.

Diffusion Models: A New Paradigm for AI Image Generation

What are Diffusion Models?

Diffusion Models are a class of powerful generative models that create high-quality images from random noise through a gradual denoising process.

Core Concepts

Diffusion models involve two key processes:

1. Forward Diffusion Process (Adding Noise)

Gradually add Gaussian noise to data until it becomes pure noise:

$$ x_t = \sqrt{\alpha_t} x_0 + \sqrt{1-\alpha_t} \epsilon $$

2. Reverse Denoising Process (Generation)

Train a neural network to learn the reverse process, recovering data from noise:

Introduction to Reinforcement Learning: From Zero to AlphaGo

What is Reinforcement Learning?

Reinforcement Learning (RL) is an important branch of machine learning that studies how agents learn optimal policies in an environment through trial and error.

Core Concepts

• Agent: The subject that learns and makes decisions
• Environment: The world in which the agent operates
• State: The current situation of the environment
• Action: Operations the agent can perform
• Reward: Feedback signal from the environment about actions

Difference Between RL and Supervised Learning

Mathematical Framework: Markov Decision Process

RL problems are typically modeled as Markov Decision Processes (MDP):