leehanchung / Fullstackmachinelearning
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Guides and Resources Full Stack Machine Learning Engineering
This is curated guide of resources and case studies for full stack machine learning engineering, break down by topics and specializations. Python is the preferred language of choice as it covers end-to-end machine learning engineering.
The curated resources will be focusing around the infamous hidden technical debt of machine learning paper by Google. Note that machine learning models, while crucial, requires a lot of engineering services and effort to be productized to generate business values. For those who does not have a technical background in or wants some refreshers of computer science, please visit the computer science section.
Data Engineering
Data Engineering Frameworks
Machine Learning Model Training
Case Studies
Model Training Frameworks
Machine Learning Model Serving
If a model was trained on a computer and no API is around to serve it, can it make an inference?
🏫 Courses
Berkeley: Full Stack Deep Learning ⭐️
Udemy: Deployment of Machine Learning Models ⭐️
Udemy: The Complete Hands On Course To Master Apache Airflow
Model Serving Frameworks
Machine Learning Operations (MLOps)
Case Studies and Tutorials
Tutorial: From Notebook to Kubeflow Pipeline
How to version control your production machine learning models
GCP: Kubeflow Pipeline Tutorial
Continous Integration/Continous Delivery
Github: Github Actions Tutorial
Github Actions ML Ops abuse repo
Pipeline Tools
Machine Learning Project Design Case Studies
📰 Articles
Microsoft: The Team Data Science Process lifecycle
Microsoft: Software Engineering for Machine Learning
Google: Machine Learning: The High Interest Credit Card of Technical Debt
Amazon: Introducing the Well Architected Framework for Machine Learning
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
Software Engineering for Machine Learning: A Case Study
Toutiao (ByteDance/Tik-Tok): Recommendation System Design
DailyMotion: Industrializing Machine Learning Pipelines
Paypal: On a Deep Journey towards Five Nines
Machine Learning Modeling
Fundamentals of machine learning, including linear algebra, vector calculus, and statistics.
📚 Textbooks
Mathematics for Machine Learning
The Elements of Statistical Learning
Pattern Recognition and Machine Learning: [Codes]
🏫 Courses
MIT 18.05: Introduction to Probability and Statistics ⭐️
Stanford Stats216: Statiscal Learning ⭐️
edX ColumbiaX: Machine Learning
Stanford CS229: Machine Learning
Stanford CS246: Mining Massive Data Sets
Artificial Intelligence
Machine learning is a sub field of Artificial Intelligence. These courses provides a much higher level understanding of the field of AI.
📚 Textbooks
Artificial Intelligence: A Modern Approach
🏫 Courses
Berkeley CS188: Artificial Intelligence ⭐️
edX ColumbiaX: Artificial Intelligence: [Reference Solutions]
Deep Learning Overview
Basic overview for deep learning.
🏫 Courses
Deeplearning.ai Deep Learning Specialization: [Reference Solutions] ⭐️
Specializations
Recommendation Systems
Vision
📚 Textbooks
🏫 Courses
Stanford CS231n: Convolutional Neural Networks for Visual Recognition: [Assignment 2 Solution, Assignment 3 Solution] ⭐️
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: [Reference Solutions]
Natural Language Processing
With languages models and sequential models, everyone can write like GPT-2.
📚 Textbook
Introduction to Natural Language Processing
🏫 Courses
Stanford CS224n: Natural Language Processing with Deep Learning: [Reference Solutions] ⭐️
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: [Reference Solutions]
Deep Reinforcement Learning
📚 Textbook
🏫 Courses
Coursera: Reinforcement Learning Specialization <= Recommended by Richard Sutton, the author of the de facto textbook on RL. ⭐️
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: [Reference Solutions]
Stanford CS234: Reinforcement Learning
Berkeley CS285: Deep Reinforcement Learning ⭐️
CS 330: Deep Multi-Task and Meta Learning: Videos
Berekley: Deep Reinforcement Learning Bootcamp
Unsupervised Learning and Generative Models
🏫 Courses
Stanford CS236: Deep Generative Models
Berkeley CS294-158: Deep Unsupervised Learning
Robotics 🤖
🏫 Courses
Computer Science
Basic computer science skill is required for machine learning engineering.
📚 Textbooks
🏫 Courses
MIT: The Missing Sememster of Your CS Education ⭐️
Corey Schafer Python Tutorials
edX MITX: Introduction to Computer Science and Programming Using Python ⭐️
edX Harvard: CS50x: Introduction to Computer Science
LICENSE
All books, blogs, and courses are owned by their respective authors.
You can use my compilation and my reference solutions under the open CC BY-SA 3.0 license and cite it as:
@misc{leehanchung,
author = {Lee, Hanchung},
title = {Full Stack Machine Learning Engineering Courses},
year = {2020},
howpublished = {Github Repo},
url = {https://github.com/full_stack_machine_learning_engineering_courses}
}