Learning Resources
This post will constantly be updated.
People who I admire
- Andrew Ng - Medium
- Andrew Ng - personal website
- Marc Cohen
- Laurence Moroney
- Sebastian Raschka
- Kyunghyun Cho
Computer Science
For General Search
- Geeks for Geeks
- Tutorials Point
- StackOverflow (for general questions)
- MIT Missing Semester (things you will never learn in CS Lectures)
Specific Topics
Architecture and OS
Logic and Compilers
Networked System
Algorithms and Data Structure
Languages
SQL
- Java and SQL (better in SQL)
Python
C
Java
Haskell
Markdown and Latex
Mathematics
- Yale Uni. Notes on Discrete Mathematics
- Mathematics for Machine Learning
- ERWIN KREYSZIG’s Engineering Mathematics
- Zill’s Engineering Mathematics
Machine learning and Deep learning
- Andrew Ng’s DL Course
- Hands on Machine Learning with Sci-kit learn and Tensorflow
- MIT Spring 2016 Introduction to Machine Learning
- Google Dev. ML Crash Course
- Google Codelabs - TensorFlow, Keras and deep learning, without a PhD
- Neural Net Visualisation
- Stock Prediction (project of boris)
- Computer Vision Visualisation
- Fullstack Deep Learning - March 2019
- Course materials for General Assembly’s Data Science course in Washington, DC
- Hands on ML Ipython files
- Kyunghyun Choi’s NYU course materials
- Neural Networks and Deep Learning, Springer, September 2018 Charu C. Aggarwal. (book)
- Advanced NLP with spaCy (good NLP tutorial)
- pyimagesearch (good Computer Vision tutorial)
- SandDance (Microsoft’s autamated EDA Tool. Available on VScode)
- NLTK Tutorials
- Ian Goodfellow and Yoshua Benegio’s Deep Learning Lecture series
- Bloomberg’s Foundation of ML
- Deep Learning with PyTorch - Book in pdf
- Daniel Bourke’s self-created AI Masters Degree
- AI Glossary (suggested by Fullers Library)
ML/DL in Korean
- Hunkim’s 모두를 위한 머신러닝
- wikidocs NLP (NLP Explanation in Korean)
- 패스트 캠퍼스 Pytorch course materials (korean)
- DeepLearning Zero to All (Tensorflow and Pytorch tutorials in korean)
- Tensorflow Basics (korean)
- Tensorflow Examples (korean)
- Tensorflow Tutorials (korean)
- 논문 리뷰 블로그
- 송호연님의 인공지능 블로그
Reinforcement Learning
Quantum Computing
- Coding with Qiskit
- IBMQ Qiskit documentation
- IBM Quantum Experience
- DWaves - Cloud-base Quantum Computer
Dataset
- Amazon Product Data
- Stanford Movie Dataset (Good for Sentimental Analysis)
- Kaggle Datasets (Almost perfectly Refined Data)
- PDFdrive (For finding PDFs)
- Library Genesis
Lecture Series on Youtube
- MIT 6.S191 2019 (MIT Deep-Learning Lectures)
- Stanford CS229 by Andrew Ng
- Stanford CS231n Computer Vision Lecture series
- UCL X Deepmind 2020 - Intro to ML and DL Series
- UCL X Deepmind 2018 - Advanced DL and RL Series
- Harvard CS50 (Harvard lectures on general CS)
- MIT OpenCourseWare
- 3Blue1Brown (Math and ML Visual Explantion)
- Tensorflow official channel
- Computerphile (cs topics explained)
- Neso Academy (cs topics explained)
- Deeplearning.ai (Andrew Ng)
- CS Dojo
- Two Minute Papers
- 생활코딩 (korean. 이고잉님)
Stanford CS Package
Natural Language Processing
- CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
- CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)
- CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)
- CS 276: Information Retrieval and Web Search (LINGUIST 286)
Computer Vision
- CS 131: Computer Vision: Foundations and Applications
- CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning
- CS 231N: Convolutional Neural Networks for Visual Recognition
- CS 348K: Visual Computing Systems
Others
- CS224W: Machine Learning with Graphs(Yong Dam Kim )
- CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)
- CS 236: Deep Generative Models
- CS 228: Probabilistic Graphical Models: Principles and Techniques
- CS 337: Al-Assisted Care (MED 277)
- CS 229: Machine Learning (STATS 229)
- CS 229A: Applied Machine Learning
- CS 234: Reinforcement Learning
- CS 221: Artificial Intelligence: Principles and Techniques
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