Contains material relevant to "Deep Into CNN" Project.
- Local Setup (Use Conda : recommended)
https://jupyter.readthedocs.io/en/latest/install/notebook-classic.html
https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#installation - (Optional: Basic Python and libraries)
https://duchesnay.github.io/pystatsml/index.html#scientific-python - ( Optional : For those with very basic ml knowledge: Only 2.1-2.7)
https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN - Linear Regression:
https://medium.com/analytics-vidhya/simple-linear-regression-with-example-using-numpy-e7b984f0d15e - Logistic Regression:
https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc
Find in NeuralNetIntro : W2-3.
- This one is highly recommended:
https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
Some more material (bit extensive, so be careful):
https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI - Basic Backprop:
https://ml-cheatsheet.readthedocs.io/en/latest/backpropagation.html - Backprop (Mathematical Version):
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ - Softmax:
https://ljvmiranda921.github.io/notebook/2017/08/13/softmax-and-the-negative-log-likelihood/ - Pytorch(Skip the CNN part if you want for now):
https://pytorch.org/tutorials/beginner/basics/intro.html - Optional guide:
http://neuralnetworksanddeeplearning.com/chap1.html - Pytorch Autograd:
https://www.youtube.com/watch?v=MswxJw-8PvE&list=PL-bzqKhHrboYIKgBwoqzl6-eyCHP3aBYs&index=4
Find in PyTorch : W2-3.
June 1 - June 30 :
https://www.kaggle.com/c/tabular-playground-series-jun-2021
- These will give you a good Intuition:
https://www.youtube.com/watch?v=py5byOOHZM8
and
https://www.youtube.com/watch?v=BFdMrDOx_CM
Also, do check this blog out:
https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53 - Highly Recommended:
https://cs231n.github.io/convolutional-networks/ - (L2-L11 : Enough for Understanding Implementation Details):
https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF - Pytorch official guide (Try after doing W3 exercises):
https://pytorch.org/tutorials/beginner/basics/intro.html
Find in W3 Folder