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Deep Learning

This has all my notes notes for Deep Learning theory, papers, implementation details, etc.

PreProcessing Convolution Arithmetic Regularization Optimizers Loss function Activation Functions Automatic Differentiation Softmax Classifier and Cross Entropy Normalization Batch Normalization Variational Inference VAE: Variational Auto-Encoders Generative vs Discriminative Making GANs train Dimensionality of Layer Vs Number of Layers Deep learning techniques Dilated Convolutions Non-Maximum Suppression Hard Negative Mining Mean Average Precision Fine Tuning or Transfer Learning Hyper-parameter Tuning

PreviousBias Variance Trade-off NextPreProcessing

Last updated 6 years ago