jyotipmahes/ML-Interview-Prep

选用指南

AI 根据 README 整理 · 2026/11/1 11:21:30

适用场景

适合准备机器学习工程师技术面试，需要从零实现核心算法并理解数学原理的场景。

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# ML Interview Prep Repository for Machine Learning Engineer (MLE) interview preparation materials and from-scratch algorithm implementations. Additional notebooks and resources will be added here. --- ## 📌 Current Status ### Available Notebooks - **[`MLE_Coding_Interview_Guide.ipynb`](./MLE_Coding_Interview_Guide.ipynb)**: From-scratch implementations of core MLE coding interview algorithms with mathematical formulations, interview tips, and verification tests: 1. **Linear Regression** (Vectorized Gradient Descent & Normal Equation)

目标用户

面向机器学习工程师（MLE）面试备考者。

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# ML Interview Prep Repository for Machine Learning Engineer (MLE) interview preparation materials and from-scratch algorithm implementations. Additional notebooks and resources will be added here. --- ## 📌 Current Status ### Available Notebooks - **[`MLE_Coding_Interview_Guide.ipynb`](./MLE_Coding_Interview_Guide.ipynb)**: From-scratch implementations of core MLE coding interview algorithms with mathematical formulations, interview tips, and verification tests: 1. **Linear Regression** (Vectorized Gradient Descent & Normal Equation)

使用方式

通过Jupyter Notebook交互式学习，包含实现、数学公式和验证测试。

查看 README 依据

# ML Interview Prep Repository for Machine Learning Engineer (MLE) interview preparation materials and from-scratch algorithm implementations. Additional notebooks and resources will be added here. --- ## 📌 Current Status ### Available Notebooks - **[`MLE_Coding_Interview_Guide.ipynb`](./MLE_Coding_Interview_Guide.ipynb)**: From-scratch implementations of core MLE coding interview algorithms with mathematical formulations, interview tips, and verification tests: 1. **Linear Regression** (Vectorized Gradient Descent & Normal Equation)

2. **Logistic Regression** (Vectorized Gradient Descent & Numerically Stable Sigmoid) 3. **K-Means Clustering** (Random Initialization, Vectorized Broadcasting & List Comprehension) 4. **K-Nearest Neighbors (KNN)** (Unified Pairwise Distance Matrix & $\mathcal{O}(N)$ Top-K) 5. **2-Layer MLP with Backprop** (Manual Multivariate Chain Rule in pure NumPy) 6. **Layer Normalization** (NumPy & PyTorch implementations) 7. **Multi-Head Self-Attention** (Scaled Dot-Product, Causal Masking & Projections in PyTorch)