Notebooks for MLRS 2019. This repo contains the notebooks with only simplified code blocks. The lecture slides will be uploaded later.
[Note: The installation slides can be find here.]
Check step-by-step instructions to setup the running environments on AWS EC2. Please make sure to submit a limit increase by the noon of the first day of MLRS2019.
What is more, if you would like to install locally (Mac and Linux available), check step-by-step instructions here.
| Title | Jupyter Notebooks | Slides |
|---|---|---|
| Machine Learning Basics | N/A | ML Basics |
| Deep Learning Basics | jupyter | DL Basics |
| Advanced Optimization | N/A | Advanced Optimization |
| Convolution Neural Network | jupyter | CNN (morning session) CNN (afternoon session) |
| Recurrent Neural Network | jupyter | RNN |
| Title | Website | Notes |
|---|---|---|
| Introduction to Deep Learning | Course | Introductory to medium level deep learning course (that we taught at UC Berkeley). Lecture videos, slides, homework, etc. are all available online. |
| Introduction to Machine Learning | Course | Introductory level machine learning course. Lecture videos, slides, homework, etc. are all available online. |
| The Foundations of Data Science | Course | Introductory level machine learning course. Lecture videos, slides, homework, etc. are all available online. |
| Deep Reinforcement Learning | Course | Advanced level reinforcement learning course. Lecture videos, slides, homework, etc. are all available online. |
| Dive into Deep Learning | Textbook | Textbook with interactive jupyter notebook to get your hand dirty on deep learning. |
| GluonCV | GitHub | A python toolkit which provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision. |
| GluonNLP | GitHub | A python toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research. |
| GluonTS | GitHub | A python toolkit for probabilistic time series modeling, which provides utilities for loading and iterating over time series datasets, state of the art models ready to be trained, and building blocks to define your own models and quickly experiment with different solutions. |
| MXNet Online Discussion | Website | An online protal to discuss deep learning, machine learning, how to do things efficiently in MxNet/Gluon, ask for help, or suggest new things. |
Licensed under CC BY-NC-SA.
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