Deep-Learning-Resources

Resources for deep learning

Below are some of the best resources I've come across for deep learning across various topics. These are probably only resources you need to watch/read if you are refreshing your knowledge or learning from scratch. This list specifically targets Convolution Neural Networks for imaging applications and in general theory of deep learning.

obiviously I've not gone through all the material available on the net on the topic of deep learning. But, whatever I've come across these are the best ones

Installation guide for tensorflow, keras on windows

• https://www.youtube.com/watch?v=z0qhKP2liHs

One additional problem I came across was that tensorflow only works with python 3.6. So, one needs to set environment for python 3.6 and then do a pip install.

stackoverflow link

Deep learning theory

Best cource available.

• https://cs231n.github.io/

Good Summery of all the nuts and bolts

• https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/

• https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/

CNN nitty gritty

vanishing gradient problem and ReLu

• https://www.quora.com/What-is-the-vanishing-gradient-problem

• https://stats.stackexchange.com/questions/126238/what-are-the-advantages-of-relu-over-sigmoid-function-in-deep-neural-networks

how exactly filter updates work in CNN

• https://datascience.stackexchange.com/questions/25754/updating-the-weights-of-the-filters-in-a-cnn

• https://ai.stackexchange.com/questions/5769/in-a-cnn-does-each-new-filter-have-different-weights-for-each-input-channel-or

non-linearity

• https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0

CNN archietectures

Overview of all popular archietectures

• https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html

High level understanding of CNN / deep learning

Two lectures on 'why to do deep learning' than 'how to do deep learning'

• https://www.youtube.com/watch?v=u6aEYuemt0M

• https://www.youtube.com/watch?v=F1ka6a13S9I

Brief overview of 'why deep learning'

• https://www.mathworks.com/discovery/deep-learning.html

Deep learning with tensorflow

One of the best hands-on demo.

• https://www.youtube.com/watch?v=vq2nnJ4g6N0&t=6467s

Tensorflow

TF cource

• http://web.stanford.edu/class/cs20si/syllabus.html

Comprehensive TF resources

• https://github.com/astorfi/TensorFlow-World-Resources

• https://github.com/jtoy/awesome-tensorflow

Deep learning in deapth hands on

Go through entire series

• https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ

Transfer learning

Skip to hand-on example part

• https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a

CNN source codes to go through

• https://github.com/sjchoi86/Tensorflow-101

• https://github.com/Mrgemy95/Tensorflow-Project-Template

• https://github.com/PacktPublishing/Hands-on-Convolutional-Neural-Networks-with-Tensorflow/blob/master/Chapter02/cnn_mnist.py

• https://github.com/ageron/handson-ml/blob/master/13_convolutional_neural_networks.ipynb

• https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb

• https://github.com/tensorflow/models

• https://github.com/dipanjanS/hands-on-transfer-learning-with-python/tree/master/notebooks

• https://github.com/ageron/handson-ml

Visualization of CNN's

Demo of deep learning visualization toolbox. Showing a visual interpretation of what exactly network 'learns'.

• https://www.youtube.com/watch?v=AgkfIQ4IGaM

Where deep learning fails

Interesting insight of CNN identifying random images with high confidence.

• https://www.youtube.com/watch?v=M2IebCN9Ht4

eBooks

• https://github.com/changwookjun/StudyBook