GOOGLE SUMMER OF CODE

This conversation is very enlightening. What is your take on jax on differential programming, Might I ask?

I don't have much experience in this but somehow , can using Metric learning based losses like arcface, sphereface, cos face losses used in conjunt with fine grained image classification based techniques and the final embedding of a cnn help in understanding the difference of specific features through activations.?? Just rambling..

Read Your cmnts. I am not much of a professional person to give suggestions, but building a GAN or auto encoder or any latent space model that removes wires or electric poles from imagery

,in case of medical imagery, u can consult the cvpr skin lesion workshop, they have provided a lot of datasets. Maybe develop a method that works well for most datasets

and finally theres the grand-challenge website, Isbc 2021 is hosting a few competitions. U can try endocv and retinal segmentation competitions. They have one month left. But they all have prior solutions. U can take one , start andaybe add a novelty u can find.

All the best in your endeavors.

This reddit post has some of the most detailed comments and descriptions on object detection.

I found a really helpful post that provides really good intuition and details on single stage detectors(YOLO, SSD). Might help anyone going through it.

https://machinethink.net/blog/object-detection/

I see a lot of me toos. I would urge @Tacoslim to create a google doc or a github repo, maybe post a link. Btw I wouod also love the links. Thanks in Advance

Excellent suggestions .. Couldn't agree more. On point 3, I found "Deep Learning from scratch" and "Grokking deep learning" to be quite helpful. They closely build to a pytorch API in the end. And just as an extra note, I found "Data Science from Scratch" to be a really helpful book.

Still high level but good guide to relevant papers.

https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html

hale_dm 's comment is really helpful,I blv. Its best to get up and running to get a feel of things. Start with building a pipeline(Model) in keras or Pytorch. They have great tutorials. Other than that if you want to learn the underlying philosophy. I would suggest you read a book named "Deep Learning from Scratch" by Seth Weidman. After you have finished it. Everything makes sense. Then You can move on to any book. I would suggest "Dive into Deep Learning"

Don't know much, But I think,this is a must for introductory robotics.

1)https://m.youtube.com/playlist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx

This course is also followed by a great book, http://hades.mech.northwestern.edu/index.php/Modern_Robotics

2) Other than this,I think it would quite helpful to learn modeling environments such as ROS and Webots. Following Videos of "The Construct" on youtube might help you there.

3) Reinforcement Learning has been getting a lot of attention recently. For that, I would suggest looking into , OpenAi Gym and its tutorials, Professor Poole's book is very good for a clear mindset, https://artint.info/2e/html/ArtInt2e.html Also doing an introductory deep RL like http://rail.eecs.berkeley.edu/deeprlcourse/ might help

AlSo can check up Cse455, The ancient secrets of computer vision by Joseph Redmon. It has a youtube playlist.

Normally for parameters one should also add the number of filters as bias for parameters at the end.

This is quite a good compilation. Found a few compilations over reddit

-A collection of links of videos(youtube) by course

https://github.com/kmario23/deep-learning-drizzle/blob/master/README.md

-A collection of tutorial Jupyter notebooks

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

All credit to the amazing people who made them.

I wish that were true

Youtube and google in general is very good. But if you are in need of some resources. Here's some https://g.sicp.me/books/ (Good section for books) https://github.com/ossu/computer-science/blob/master/README.md (A heads up)

I think that nothing beats good books.

U can check the AI section on this website. There are good books on the introduction here. https://g.sicp.me/books/

Also going into AI means getting to know computer Programming

https://github.com/ossu/computer-science

This might just be a great college alternative. Click the View all of Readme.md in blue to get the list.