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Learn more about regularization dropout in deep learning and how they work and how to implement them in tensorflow.
Learn more about the hyperparameter tuning like learning rate, number of batches, number of epochs, number of hidden layer, activation functions.
Learn how to build a neural network, and how to build layers of the neural network.
How to use Neural Networks
We will learn Loss function for deep machine learning and deep learning and some algorithms to optimize the model like Backpropagation and Gradient Descent.
Learn the mathematics behind Deep Learning including Linear Algebra, Matrices and Vectors
