Update summer_course2020.md

Author: MMathisLab

summer_course2020.md

@@ -1,6 +1,6 @@
 ## Got Behavior? Get Poses ...  <img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1572296495650-Y4ZTJ2XP2Z9XF1AD74VW/ke17ZwdGBToddI8pDm48kMulEJPOrz9Y8HeI7oJuXxR7gQa3H78H3Y0txjaiv_0fDoOvxcdMmMKkDsyUqMSsMWxHk725yiiHCCLfrh8O1z5QPOohDIaIeljMHgDF5CVlOqpeNLcJ80NK65_fV7S1UZiU3J6AN9rgO1lHw9nGbkYQrCLTag1XBHRgOrY8YAdXW07ycm2Trb21kYhaLJjddA/DLC_logo_blk-01.png?format=1000w" width="350" title="DLC-live" alt="DLC LIVE!" align="right" vspace = "50">
 
-This document is a **WIP** outline of resources for an informal "spring/summer course" for those wanting to learn to use DeepLabCut while responsibilty isolating due to COVID-19.
+This document is an outline of resources for an informal "spring/summer course" for those wanting to learn to use DeepLabCut while responsibilty isolating due to COVID-19. We except it to take roughly 1 week to get through.
 
 www.deeplabcut.org 
 
@@ -14,6 +14,8 @@ You need: Anaconda for python3 and DeepLabCut installed (CPU version)
 - you should have a [CPU version of DeepLabCut installed on your laptop](https://github.com/AlexEMG/DeepLabCut/blob/master/conda-environments/README.md). We will assume you don't all have GPUs at home, so we will 
 utilize cloud-computing resources for those steps. 
 
+ - **ACTION:** [Install DeepLabCut](https://github.com/AlexEMG/DeepLabCut/blob/master/docs/installation.md)
+
 ## Outline:
 
 ### **The basics:**
@@ -22,20 +24,21 @@ utilize cloud-computing resources for those steps.
 
 - **Learning:** although minimal to no Python coding is required (i.e. you could use the DLC GUI to run the full program without it), here are some resources you may want to check out. [Software Carpentry: Programming with Python](https://swcarpentry.github.io/python-novice-inflammation/)
 
+- **Learning:** Watch a talk from Alexander Mathis (a lead DeepLabCut developer) [talk about DeepLabCut!](https://www.youtube.com/watch?v=ZjWPHM0sL4E)
+
 - **DEMO:** Can I DEMO DEEPLABCUT (DLC) quickly? 
     - Yes: [you can click through this DEMO notebook](https://colab.research.google.com/github/AlexEMG/DeepLabCut/blob/master/examples/COLAB_DEMO_mouse_openfield.ipynb)
     - AND follow along with me: [Video Tutorial!](https://www.youtube.com/watch?v=DRT-Cq2vdWs)
+    
 
-- **WATCH:** How do you know DLC is installed properly? (i.e. how to use our test script!)[Video Tutorial!](https://youtu.be/IOWtKn3l33s)
+- **WATCH:** How do you know DLC is installed properly? (i.e. how to use our test script!) [Video Tutorial!](https://youtu.be/IOWtKn3l33s)
 
 
 <img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1587608364285-A8R2F24K4DCP0KLAYI91/ke17ZwdGBToddI8pDm48kOhrDvKq54Xu9oStUCFZX0R7gQa3H78H3Y0txjaiv_0fDoOvxcdMmMKkDsyUqMSsMWxHk725yiiHCCLfrh8O1z4YTzHvnKhyp6Da-NYroOW3ZGjoBKy3azqku80C789l0p4XabXLlNWpcJMv7FrN_NLe3GEN018us8vX03EdtIDHsW7dEh7nvL5CemxAxOy1gg/EKlIEXyXUAE0cy3.jpeg?format=1000w" width="350" title="DLC" alt="review!" align="right" vspace = "50">
 
 - **REVIEW PAPER:** The state of animal pose estmiation w/deep learning i.e. "Deep learning tools for the measurement of animal behavior in neuroscience" [arXiv](https://arxiv.org/abs/1909.13868) & [published version](https://www.sciencedirect.com/science/article/pii/S0959438819301151)
 
 
-
-
 - **WATCH:** There are a lot os docs... where to begin: [Video Tutorial!](https://www.youtube.com/watch?v=A9qZidI7tL8) 
 
 ### **Module 1: getting started on your own data**
@@ -60,6 +63,8 @@ You can use our demo videos, grab some from the internet, or use whatever older
    - **Slides:** [Overview of creating training and test data, and training networks](https://github.com/DeepLabCut/DeepLabCut-Workshop-Materials/blob/master/part2-network.pdf)
    - **READ ME PLEASE:** [What are convolutional neural networks?](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53) 
 
+   - **READ ME PLEASE:** Here is a new paper from us describing challenges in robust pose estimation, why PRETRAINING really matters - which was our major scientific contribution to low-data input pose-estimation - and it describes new networks that are availble to you. [Pretraining boosts out-of-domain robustness for pose estimation](https://paperswithcode.com/paper/pretraining-boosts-out-of-domain-robustness)
+   
   Before you create a training/test set, please read/watch:
    - **More information:** [Which types neural networks are available, and what should I use?](https://github.com/AlexEMG/DeepLabCut/wiki/What-neural-network-should-I-use%3F)
    - **WATCH:** Video tutorial 1: [How to test different networks in a controlled way](https://www.youtube.com/watch?v=WXCVr6xAcCA)