In this course, you are going to learn what is and how to implement a Neural Network model. To do this, you will use the library Pytorch using Python programming language, a tool that allows you to easily build a neural network model.

In the first part of the course, you will learn the basic about the theory of Deep Learning and then we will go deeper in the mathematics behind neural networks so that you can understand how these models works, how they are trained and evaluated and with this knowledge, you can change stuff in order to improve your own models.

After the theoretical part, you will build a practical project in which you will build a neural network model to classify plants into different species. To do this you will use Pytorch, a library that allows you to create and train a neural network model using Python. First, you will create the model using a specific architecture, and then you will train the model by applying all the concepts you learned in the theoretical part. After training, you will get metrics that will allow you to analyze and evaluate how good is your model. Based on the results, you will learn how to implement changes into your project so that you can learn how to explore different options in order to improve the model perfomance.

You don't need previous knowledge on Deep Learning or Pytorch, because you will learn the basics you need in order to build a deep learning project using this technology.

So, get started building deep learning projects with this very practical course.