Deep Learning certificate

Deep_Learning

This repository contains examples of popular deep learning algorithms implemented in Python.

Artificial Neural Networks

In this part you will learn:

• The Intuition of ANNs

• How to build an ANN

• How to predict the outcome of a single observation (Homework Challenge)

• How to evaluate the performance of an ANN with k-Fold Cross Validation

• How to tackle overfitting with Dropout

• How to do some Parameter Tuning on your ANN to improve its performance

Convolutional Neural Networks

In this part you will learn:

• The Intuition of CNNs

• How to build an CNN

• How to predict what is inside a single image (Homework Challenge)

• How to improve a CNN

Recurrent Neural Networks

In this part, we will take part in a real R&D process to build a robust and relevant Recurrent Neural Network. Here is the plan of attack:

• We will study the theory and get the Intuition of RNNs.

• We will start by building a simple RNN, our first attempt.

• We will observe the results to identify possible issues and ways of improvement, so that eventually this simple RNN will be well improved in the last section.

• We will learn how to evaluate a RNN model, and more generally a Regression model.

Self Organizing Maps

In this part you will learn:

• The Intuition of SOMs

• How to build a SOM

• How to return the specific features (like frauds) detected by the SOM

• How to make a Hybrid Deep Learning Model

Let's see an example