This course aims to introduce deep learning concepts to a broad audience. By utilizing Keras and TensorFlow in R, participants will learn regression, linear algebra, derivatives, gradient descent, neural networks, logistic regression, bias and variance, regularization, dropout, and improving learning in deep neural networks. The course employs a hands-on approach, teaching through examples and practical exercises. It is designed for individuals interested in deep learning, with a focus on using R for implementation.
Overview
Syllabus
Regression as a first step in deep learning.
Linear regression as a simple learner.
Basic linear algebra for deep learning.
Basic derivatives for deep learning.
Gradient descent.
Linear regression as a shallow neural network.
Logistic regression as a network.
Simple neural network.
Introduction to R for deep learning.
Example of a deep neural network using Keras in R.
Bias and variance in deep learning.
Regularization in deep learning.
Dropout in deep learning.
Regularization and dropout using Keras for R.
Improving learning in deep neural networks.
Using tfruns to compare models.
Exploring sequential models in Keras for R.
The cross entropy loss function.
Deep neural networks for regression problems.
Introduction to convolutional neural networks.
Example of a convolutional neural network.
Convolutional neural network using Keras for R - SKIN LESIONS.
Taught by
Dr Juan Klopper
