After today’s lecture (lesson9) it’s clear what pace is going to be maintained throughout this course. 
Hence it is important to start working on the exercises and readings from day one to retain our grasping pace.
Below is a rough list of all possible readings and resources for this week:
Research Papers:
- YOLO - https://pjreddie.com/media/files/papers/YOLOv3.pdf
- SSD - https://arxiv.org/pdf/1512.02325.pdf
- RetinNet - https://arxiv.org/abs/1708.02002
- MSC-MultiBox - https://arxiv.org/abs/1412.1441
Related Articles and Videos:
- Understanding SSD for real time object detection -
https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab - Understanding Anchors through Excel -
https://docs.google.com/spreadsheets/d/1ci7KMggF-_4kv8zRTE0B_u7z-mbrKEzgvqXXKy4-KYQ/edit?usp=sharing - Spatial Transforms -
http://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html - RCNN CS231n -
https://youtu.be/nDPWywWRIRo?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv
Important Additional Readings:
- Understanding cyclic learning rate -
https://arxiv.org/abs/1506.01186, http://forums.fast.ai/t/understanding-use-clr/13969 - Utilizing the efficiency of pandas as suggested by @binga in his notebook -
https://gist.github.com/binga/336258dd5965e77df6b8744b87154164, https://tomaugspurger.github.io/modern-1-intro.html - Pathlib understanding -
http://pbpython.com/pathlib-intro.html - Great resource to understand VAEs -
https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf
This list is in no manner exhaustive, so please add any additional readings/resources you find useful. 
Super charged after today’s lesson 
What do you suggest our approach should be with respect to other video resources like the CS231n lecture above? Though they are great, but require a time investment which could be could also be spent implementing the models taught in today’s lesson. @jeremy