Machine Learning

Plan of attack

The Neuron

The Activation Function

How do Neural Networks work?

How do Neural Networks learn?

Gradient Descent

Stochastic Gradient Descent

Backpropagation

Business Problem Description

Artificial Neural Networks in Python

• Installing Theano, Tensorflow and Keras

• Part 1 - Data Preprocessing

  • Importing the libraries

  • Importing the dataset

  • Encoding categorical data

  • Splitting the dataset into the Training set and Test set

  • Feature Scaling

• Part 2 - Making the ANN

  • Importing the Keras libraries and packages

  • Initialising the ANN

  • Adding the input layer and the first hidden layer

  • Adding the second hidden layer

  • Adding the output layer

  • Compiling the ANN

  • Fitting the ANN to the Training set

Part 3 - Making the predictions and evaluating the model

• Predicting the Test set results

• Making the Confusion Matrix

Artificial Neural Networks in R

• Importing the dataset

• Encoding the categorical variables as factors

• Splitting the dataset into the Training set and Test set

• Feature Scaling

• Fitting ANN to the Training set

• Predicting the Test set results

• Making the Confusion Matrix

Plan of attack

What are convolutional neural networks?

Step 1 - Convolution Operation

Step 1(b) - ReLU Layer

Step 2 - Pooling

Step 3 - Flattening

Step 4 - Full Connection

Softmax & Cross-Entropy

Convolutional Neural Networks in Python

• Installing Theano, Tensorflow and Keras

Part 1 - Building the CNN

• Importing the Keras libraries and packages

• Initialising the CNN

• Step 1 - Convolution

• Step 2 - Pooling

• Adding a second convolutional layer

• Step 3 - Flattening

• Step 4 - Full connection

• Compiling the CNN