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Regularization in deep learning
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Classroom Contents
Introduction to Deep Learning for Everyone
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- 1 Regression as a first step in deep learning
- 2 Linear regression as a simple learner
- 3 Basic linear algebra for deep learning
- 4 Basic derivatives for deep learning
- 5 Gradient descent
- 6 Linear regression as a shallow neural network
- 7 Logistic regression as a network
- 8 Simple neural network
- 9 Introduction to R for deep learning
- 10 Example of a deep neural network using Keras in R
- 11 Bias and variance in deep learning
- 12 Regularization in deep learning
- 13 Dropout in deep learning
- 14 Regularization and dropout using Keras for R
- 15 Improving learning in deep neural networks
- 16 Using tfruns to compare models
- 17 Exploring sequential models in Keras for R
- 18 The cross entropy loss function
- 19 Deep neural networks for regression problems
- 20 Introduction to convolutional neural networks
- 21 Example of a convolutional neural network
- 22 Convolutional neural network using Keras for R - SKIN LESIONS
