This repository is a curated collection of machine learning interview questions and answers, primarily focused on algorithms and concepts relevant to machine learning engineering roles. It serves as a study guide for individuals preparing for technical interviews in the field.

How It Works

The repository is structured thematically, covering fundamental machine learning concepts, classical algorithms, deep learning architectures, and essential tools. Each topic is broken down into specific questions, often with detailed explanations, mathematical derivations, and code snippets. The content aims to provide a comprehensive understanding of the underlying principles and practical applications.

Quick Start & Requirements

• Installation: No specific installation is required as this is a documentation repository.
• Requirements: Basic understanding of machine learning concepts, algorithms, and mathematics is assumed.
• Resources: The README provides a detailed table of contents and links to external resources for further study.

Highlighted Details

• Comprehensive Coverage: Encompasses a wide range of topics from basic ML concepts (loss functions, evaluation metrics) to advanced deep learning (CNNs, RNNs, LSTMs) and popular tools (Spark, XGBoost, TensorFlow).
• Mathematical Rigor: Includes mathematical derivations for key algorithms and concepts, such as backpropagation, loss function gradients, and probability distributions.
• Practical Insights: Offers explanations on practical aspects like feature engineering, hyperparameter tuning, and handling imbalanced datasets.
• Algorithm Comparisons: Frequently compares and contrasts different algorithms (e.g., GBDT vs. XGBoost, LSTM vs. GRU), highlighting their strengths and weaknesses.

Maintenance & Community

• Contributors: The repository is maintained by zhengjingwei.
• Community: No specific community links (like Discord or Slack) are provided in the README.

Licensing & Compatibility

• License: The repository does not explicitly state a license in the README.
• Compatibility: Content is primarily text-based, making it broadly compatible.

Limitations & Caveats

The repository is a static collection of information and does not include runnable code or interactive demos. The depth of coverage for some advanced topics might vary, and practical implementation details beyond conceptual explanations are limited.