- Notes and Books
- Research
- Managing
- Technical blog posts
- Tutorials
I have listed the resources in the drop-down extensions below!
Notes and Books
- Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Introduction to Machine Learning Interviews Book, Chip Huyen
- Deep Learning Study Notes by Albert Pumarola
- Learn X in Y: Python
- From Python to Numpy
- Scientific Computing in Python: Introduction to NumPy and Matplotlib
- Rules of Machine Learning: Best Practices for ML Engineering
- The Good Research Code Handbook, Patrick Mineault
- The Little Book of Deep Learning
- Math for Machine Learning, Garret Thomas
- The Matrix Calculus You Need for Deep Learning
Research
- aideadlin.es
- You and Your Research, Richard Hamming
- Tips for Success as a New Researcher, Alex Tamkin
- Awesome Tips on various research topics by Jia-Bin Huang
- Personal Rules of Productive Research, Eugene Vinitsky
- How to do Research At the MIT AI Lab
- A Survival Guide to a PhD
- How to write the introduction, Kate Saenko
- Writing a Research Statement for Graduate School and Fellowships
- How to Read a CS Research Paper?
- How to review a paper, Nato Lambert
- Building a Culture of Reproducibility in Academic Research, Jimmy Lin
- Reproducible Research Checklist
- The Machine Learning Reproducibility Checklist
- How to Be a Successful PhD Student
- How to be organized & productive during your PhD
- How to Read Research Papers (Andrew Ng)
- Why is the winner the best? (M. Eisenmann et al.)
- Career Advice / Reading Research Papers, Andrew Ng
- How You Should Read Research Papers According To Andrew Ng (Stanford Deep Learning Lectures)
- How to Get Your CVPR Paper Rejected?
- How to do research, Bill Freeman, CSAIL, MIT
- Research Advice, Joseph Paul Cohen, Mila
- Lessons from my PhD, Austin Z. Henley
- How to navigate through the ML research information flood, Dmytro Mishkin
- An Opinionated Guide to ML Research, John Schulman
- A Year of MLC, Rosanne Liu
- AI research: the unreasonably narrow path and how not to be miserable
- Research Statement, Yong Jae Lee
- Research Statement, James Hays
- Choose Your Weapon: Survival Strategies for Depressed AI Academics, Togelius et. al.
- Compilation of Advice for ML PhD Students
- Build what you need and use what you build , Micheal Black
Managing
Technical articles
- The Early History of Computer Vision, Zbigatron
- Rocket AI: 2016’s Most Notorious AI Launch and the Problem with AI Hype
- Larry Roberts PhD Thesis, 1963
- History of computer vision contests won by deep CNNs on GPU, Jürgen Schmidhuber
- A Revised History of Deep Learning, Jean de Dieu Nyandwi
- How to start a deep learning project?
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- How to Do Data Exploration for Image Segmentation and Object Detection
- Tutorial-about-3D-convolutional-network
- A Review of Different Interpretation Methods in Deep Learning
- A Comprehensive Introduction to Different Types of Convolutions in Deep Learning
- Deep Semi-Supervised Learning
- Facebook & NYU reduce Covid hospital strain — Covid Prognosis Via Self-Supervised Learning
- Zero-Shot Learning
- List of sites/programs/projects that use OpenAI's CLIP neural network for steering image/video creation to match a text description
- Contrastive Representation Learning, Lilian Weng
- Annotated Research Paper Implementations
- Writing clean and optimized Python code, Youssef Hosni
- Python best practices even data scientists should know, Yan Gobeil
- Who could be your Jeff Dean?
- The Zen of Python, Software Engineering Fundamentals, Harvard CS197
- Python Debugger (My Gist)
- Using Google Colab with GitHub
- Open a GitHub notebook in Colab
- NeurIPS 2020 ML Code Completeness Checklist
- A template README.md for code accompanying a Machine Learning paper
- How Docker Can Help You Become A More Effective Data Scientist
- Getting started open source
- Running a Jupyter notebook from a remote server
- Accessing external data from Google Colab notebooks
- How to prevent Google Colab from disconnecting?
- Documenting Python Code and Projects
- 10 Useful Jupyter Notebook Extensions for a Data Scientist
- How to improve software engineering skills as a researcher
- Bash Scripting Tutorial for Beginners
- Everything gets a package? Yes, everything gets a package.
- Classifier Project Template, Sebastian Raschka
- r/EngineeringResumes
- From Microsoft Intern to Meta Staff Engineer: Raviraj Achar
- What is the Team Data Science Process?
- How to Write Design Docs for Machine Learning Systems, Eugene Yan
- What is MLOps?, Databricks
- Building efficient Experimentation Environments for ML Projects.
- Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning.
- Which Machine Learning Algorithm Should You Use By Problem Type?
- Ultimate AI Strategy Guide
- How I Built and Deployed a Fun Serverless Machine Learning App
- I trained a model. What is next?
- 10 tips to improve your machine learning models with TensorFlow
- Introducing DecaVision to train image classifiers with Google’s free TPUs
- How to become a skillful Data Scientist following the Decathlon Data Science Development Program, Alfonso Carta
- Data Science as a Product
- Machine learning is going real-time
- Data Scientists Don't Care About Kubernetes
- Our AI ML Startups Tech Stack
- Full Stack Deep Learning: Detecting deforestation from satellite images
- Effective testing for machine learning systems.
- Explore Computer Vision APIs, Apple Inc.
- HuggingFace Tasks: demos, use cases, models, datasets, and more
Tutorials
- CAP6412 Advanced Computer Vision - Spring 2023
- Harvard CS197: AI Research Experiences
- Deep Learning for Computer Vision, Justin Johnson, UMichigan, 2020
- CS231n: Convolutional Neural Networks for Visual Recognition
- CS229: Machine Learning, 2021
- Deep Learning Specialization
- Deep Learning, Francois Fleuret
- Deep Learning in Computer Vision with Prof. Kosta Derpanis (York University), 2021
- Data Preparation and Feature Engineering in ML
- AIMS 2020, class on Visual Recognition by Georgia Gkioxari
- Reproducible Deep Learning
- MLOps-Basics, 2022
- Lightning Bits: Engineering for Researchers
- Effective MLOps: Model Development
- Image Kernels
- Convolution Visualizer
- MLOps guide, Chip Huyen
- A Complete Machine Learning Package 2021, Jean de Dieu Nyandwi
- Deep-Learning-in-Production
- Machine Learning & Deep Learning Tutorials
- Awesome Deep Learning
- Machine Learning cheatsheets for Stanford's CS 229
- Roboflow tutorials on using SOTA computer vision models
- Hugging Face Transformers Tutorials
Inspired by https://github.com/hassony2/useful-computer-vision-phd-resources