Course Overview

Deep Learning is a powerful technique to address problems that were previously unbearable for society, due to the difficulty of translating them into computers, their computational cost, or their mathematical complexity.
With good supervision and critical thinking, many new algorithms can be designed using Neural Networks, but you are going to need to put into practice your previous knowledge of mathematics, statistics, and computer programming.

Learning Objectives

In this course, we will assume a previous background in mathematics and statistics applications. Their concepts will be used within Python code, classes and functions, and useful libraries such as Keras and Tensorflow.

Also, we will assume previous knowledge of some basic ROS functionalities, on how to create packages, start simulations, run ROS nodes, and compile their code.

You’ll be presented with the two typical challenges for unsupervised learning: regression and classification algorithms. You are going to see the key components of a neural network, neurons, and understand the role of weights, biases, activation functions, loss, and accuracy.

You’ll be presented with the hyperparameters that rule the behavior of a neural network.

You’ll learn how by inspecting the learning evolution of our algorithm we can choose to apply one or another optimization technique to improve its results.

You’ll be presented with an example of a Convolutional Neural Network, an excellent tool to solve Computer Vision problems. You’ll see which neurons and mathematical operations allow extracting features of images, and then classify them into labels. You are going to be challenged to improve their performance of them with optimization techniques.

Simulation Robots Used

TurtleBot 3