Introduction to MLE-Flashcards
Overview
The MLE-Flashcards project is a collection of over 200 flashcards designed to consolidate knowledge in machine learning (ML), computer science (CS), and specifically, computer vision and deep learning. These flashcards are a result of years of research, academic courses, and independent study by the creator, who successfully used them to prepare for machine learning engineer interviews with outcomes at companies like Google, Tesla, and TikTok in 2022. They are shared with the hope that others can also benefit from this structured learning tool.
How to Use the Flashcards
The flashcards are primarily available in PDF format for ease of use, though more dynamic and up-to-date presentations are accessible through Google Slide links. Each presentation covers a specific domain:
- Computer Science Slides focus on foundational CS topics.
- Machine Learning General Slides encompass broad ML subjects.
- Fundamentals for Computer Vision & Deep Learning Slides delve into the basics of these specific fields.
- Selected Topics in Computer Vision & Deep Learning Slides explore more nuanced topics within these subjects.
Key slides, noted for their importance, are denoted with a star for easy identification. For those interested in the very essence of the interview process in machine learning, questions are inspired by Chip Huyen’s ML Interview Book.
Who Can Benefit
The flashcards target a varied audience based on their familiarity with the subject matter:
- Individuals with a solid groundwork in ML can use the flashcards for revision and to highlight any potential knowledge gaps.
- Novices or those new to ML can gain an overview of the field. These users are encouraged to complement the flashcards with additional educational resources for a well-rounded understanding.
Contributing and Engagement
Recognizing that the field of ML and CS is ever-evolving, the creator acknowledges that the flashcards may not be exhaustive. Users spotting any errors or omissions can contribute by logging these as issues on the project's GitHub page. Specific slides can be discussed by marking them clearly (e.g., using [2.14] for a particular slide from the Machine Learning General presentation).
Additional Resources
For a comprehensive view of the MLE interview process and further learning:
- Chip Huyen's ML Interview Book offers holistic insights into preparing for MLE roles.
- Stanford and UC Berkeley’s course materials provide an extensive look into machine learning topics.
- Resources like "Cracking the Coding Interview" and LeetCode are recommended for those focusing on the programming aspects of interview preparation.
The MLE-Flashcards project is a valuable resource for anyone looking to solidify their understanding of machine learning and computer science and offers a collaborative platform for continuous improvement and learning.