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C++ Porblem Solving

Leetcode

Leetcode 50
Leetcode 704
Leetcode 1095

APCS

實作題 - 物品堆疊 - APCS - by Peter Wang
實作題 - 棒球遊戲 - APCS - by Peter Wang
資料結構題 - 定時K彈 - APCS - by Peter Wang
實作題 - 數字龍捲風 - APCS - by Peter Wang
實作題 - 購買力 - APCS - by Peter Wang
實作題 - 購物車 - APCS - by Peter Wang
實作題 - 人力分配 - APCS - by Peter Wang
實作題 - 邏輯運算子 - APCS - by Peter Wang
實作題 - 三角形辨別 - APCS - by Peter Wang
實作題 - 交錯字串 - APCS - by Peter Wang
實作題 - 秘密差 - APCS - by Peter Wang
實作題 - 骰子 - APCS - by Peter Wang
實作題 - 人口遷移 - APCS - by Peter Wang
實作題 - 流量 - APCS - by Peter Wang
實作題 - 最大和 - APCS - by Peter Wang
資料結構題 - 切割費用 - APCS - by Peter Wang
演算法題 - 圓環出口 - APCS - by Peter Wang
演算法題 - 基地台 - APCS - by Peter Wang
資料結構題 - 樹狀圖分析 - APCS - by Peter Wang
資料結構題 - 血緣關係 - APCS - by Peter Wang
演算法題 - 勇者修煉 - APCS - by Peter Wang
實作題 - 成績指標 - APCS - by Peter Wang
資料結構題 - 低地距離 - APCS - by Peter Wang
資料結構題 - 線段覆蓋長度 - APCS - by Peter Wang
實作題 - 函數運算求值 - APCS - by Peter Wang
實作題 - 矩陣翻轉 - APCS - by Peter Wang
實作題 - 小群體 - APCS - by Peter Wang

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UW HYAK Notes

Request for GPU

Mar 14, 2025
Summary: Decoder-Only Transformers: The Workhorse

Introduction This article delves into the architecture and mechanics of decoder-only transformers, which are a crucial component of many large language models (LLMs). It highlights the structure, attention mechanisms, and embedding techniques that make these models effective for various natural language processing (NLP) tasks. Decoder-Only Transformer Architecture Overview Decoder-only transformers, unlike the traditional encoder-decoder structure, use only the decoder component to process and generate text. This architecture is particularly suited for tasks that involve sequential generation, such as text completion and language modeling. Structure The decoder-only transformer consists of multiple layers, each containing self-attention mechanisms and feed-forward neural networks.

Jun 26, 2024
Summary: The Illustrated Transformer

Introduction This article provides a visual and intuitive explanation of the transformer architecture, which has revolutionized natural language processing (NLP) by enabling efficient handling of sequential data through self-attention mechanisms. It covers the structure, mechanics, and key components such as the encoder, decoder, attention mechanisms, and embeddings. Transformer Architecture Overview The transformer architecture, introduced by Vaswani et al. in 2017, eliminates the need for recurrent layers by using self-attention mechanisms, allowing for parallel processing and better handling of long-range dependencies. Encoder-Decoder Structure The transformer model consists of an encoder-decoder architecture, each composed of multiple layers.

Jun 26, 2024
Summary: Transformers Explained

This article offers a detailed explanation of transformers, a revolutionary architecture in natural language processing (NLP) that has significantly advanced the capabilities of large language models (LLMs). It covers the structure, mechanics, and key components of transformers, including the encoder, decoder, attention mechanisms, and embeddings.

Jun 26, 2024
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