Notes.txt

https://towardsdatascience.com/step-by-step-implementation-3d-convolutional-neural-network-in-keras-12efbdd7b130

https://keras.io/examples/vision/3D_image_classification/

https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly

https://www.kaggle.com/broach/trends-working-with-fmri-component-maps

https://www.kaggle.com/saife245/neuroimaging-in-depth-understanding-eda-model

https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035

https://www.youtube.com/watch?v=NJNvXEYa_gM

-> PointNet - 3-D classification of Voxels
-> Use Node2vec - feature vector -> to Fully connected layer
-> CNN-LSTM also do
-> 3-D CNN (2015 model)
-> Autoencoder model do - classification

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Regression Loss Functions
	Mean Squared Error Loss
	Mean Squared Logarithmic Error Loss
	Mean Absolute Error Loss
Binary Classification Loss Functions
	Binary Cross-Entropy
	Hinge Loss
	Squared Hinge Loss
Multi-Class Classification Loss Functions
	Multi-Class Cross-Entropy Loss
	Sparse Multiclass Cross-Entropy Loss
	K	ullback Leibler Divergence Loss