#Machine-Learning

13 posts tagged "machine-learning"

1

Part 1 — AI 工程從零開始｜Phase 1 Part 1：線性代數與微積分 — AI 演算法直覺

從工程師視角掌握 AI 必備的線性代數與微積分直覺：向量、矩陣、梯度下降、反向傳播背後的數學原理，附 ASCII 架構圖與面試答題要點

·23 min
1

Part 1 — 如何衡量 AI 的準確度（一）：分類與回歸任務的基礎評估指標

AI 準確度不是一個數字就能說清楚的。本文從分類與回歸任務出發，介紹 Precision、Recall、F1-Score、RMSE 等核心指標，幫助你建立客觀評估 AI 模型的基礎框架。

·12 min
3

Part 3 — AI 工程從零開始｜Phase 2 Part 1：傳統機器學習 — 生產 AI 的骨幹

深入解析線性回歸、邏輯回歸、決策樹、SVM、特徵工程等傳統 ML 技術為何在 80% 生產 AI 系統中仍是首選，附完整決策框架與面試要點

·23 min
3

Part 3 — FDE 面試準備指南（三）：你不能忽略的 ML 基礎

以 Google AI 工程師兼面試官的視角，整理 FDE 面試中仍然高頻的傳統 ML / AI 基礎知識，包含 Transformer、Embedding、評估指標與 Fine-tuning 的工程視角

·13 min
3

Part 3 — Crypto Quantitative Trading Part 3: Optimization, Validation, and Production Deployment

Complete guide to deploying quantitative crypto trading strategies to production. Learn validation techniques, optimization methods, live trading APIs, monitoring systems, and ML enhancements. Includes full AWS deployment architecture and Docker containerization.

·30 min
4

Part 4 — AI 工程從零開始｜Phase 2 Part 2：集成學習與最佳化 — 超越單一模型的上限

深入解析 Random Forest、Gradient Boosting、XGBoost、超參數調優與 AutoML，理解集成方法為何在表格資料競賽與生產系統持續稱霸

·23 min
8

Part 8 — FDE 面試準備指南（八）：ML 基礎必備——從傳統機器學習到 Deep Learning

以 Google AI 工程師兼面試官的視角，系統整理 FDE 面試不能缺的 ML 基礎：Supervised Learning、評估指標、Overfitting 處理，以及從 MLP 到 Transformer 的 Deep Learning 核心概念

·18 min

Building a Sentiment-Driven US Stock Trading System with X.com Real-Time Analysis

Learn how to architect a production-ready sentiment-driven stock trading platform that streams X.com posts in real-time, analyzes market sentiment using AWS Bedrock and HuggingFace, and automatically executes trades on US stocks based on social media signals.

·20 min

Building an Intelligent Bitcoin Trading System with AWS CDK and ML Models

Learn how to architect and deploy an intelligent cryptocurrency trading system on AWS using CDK, with historical price analysis, ML-powered predictions from AWS Bedrock and HuggingFace, and event-driven trade execution.

·18 min

Building AI Music Generation Platform: AWS CDK Architecture with SageMaker and Bedrock Comparison

Complete guide to architecting a production-ready AI music generation platform on AWS using CDK, comparing SageMaker and Bedrock approaches with detailed pros, cons, and implementation strategies for generating music from text prompts.

·21 min

Fine-Tuning LLMs with AWS Bedrock: A Complete Guide to Post-Training Customization

Comprehensive guide to fine-tuning and customizing Large Language Models (LLMs) with AWS Bedrock - covering supervised fine-tuning, continued pre-training, and reinforcement fine-tuning with practical examples and AWS CDK infrastructure setup.

·28 min

Deploying Hugging Face Models to AWS: A Complete Guide with CDK, SageMaker, and Lambda

Learn how to deploy production-ready Hugging Face AI models to AWS using CDK (TypeScript), SageMaker, and Lambda. Comprehensive guide covering system design, infrastructure setup, model deployment, API creation, and best practices for scalable ML applications.

·55 min

Building a Spotify Playlist Application with Spring Boot and Vue.js

使用 Spring Boot 後端與 Vue.js 前端，整合 Spotify API 打造智能音樂推薦系統，突破 Spotify 原生推薦限制，提供更主動的音樂探索體驗。

·15 min