Deep Learning-101

These Jupyter notebooks are prepared as coding practices for an introductory course for Deep Learning.

To run the notebooks, use either a virtual environment (local desktop) or Google Colab environment.

From a local desktop

Setup a python virtual environment and activate it.
From a terminal, type:
```
>pip install jupyterlab
>jupyter lab
```
Once the browser launches, run all cells of 00-setup.ipynb. This will install all necessary libraries needed for the notebooks.

The materials are created based on various sources. Here is a short list.

MIT Deep Learning: One of the best introductory lectures on Deep Learning. The lectures are easy to understand and covers extremely broad topics including reinforcement learning which is strictly not a part of Deep Learning.
모두를 위한 딥러닝: In my opinion, this is the best introduction course for Deep Learning available online in Korean. Professor Kim explains difficult machine learning concepts in down-to-earth easy-to-understand words. My only complaint is that the lab portions tend to be verbose and written in TensorFlow v1 which is quite different from TF v2. If interested, here is how to migrate v1 to v2.
TensorFlow tutorials: Of course, the official TensorFlow serves as the best authority for TensorFlow. The tutorials are fairly easy to follow and setup and there are many available examples.