Deep Learning Basics Course - Python

You will learn deep learning basics.

Course Summary

Take your first step into Neural networks the base for all the AI done nowadays.

What you will learn

Deep Learning foundations: * Basics about neural networks * Deep Neural networks * Loss, optimization, and gradient descent. * Training Deep Neural Networks * Convolutional Neural Networks

Course Overview

Introduction

See in action what you will be able to accomplish after completing this course.

Neural Networks Introduction

Understand the fundamental concepts and structure of artificial neural networks, including how to build and implement them using PyTorch.

Deep Neural Networks

Generalize the concept of Shallow Neural Network to Deep Neural Networks that have more than one hidden layer.

The Training Process

Explore the general process of training a neural network

Global Exercise

Apply what you have learned until now to teaching the Gatekeeper robot to read.

Optimization, Gradient Initialization, and Regularization

Understand the key techniques involved in training machine learning models, focusing on model fitting, gradient-based optimization, and regularization.

Convolutinal Networks

Convolutional layers are essential for handling image data efficiently. These layers make use of sparser connections, shared weights, and parallel processing paths, allowing them to capture important spatial relationships in the input image.

Teachers

Estanislao Escudero

I'm a Mechanical Engineer who looks forward to learn more about robotics and AI.

Dylan James-Kavanaugh

Mechanical Engineering Master's Student & Research Assistant