Convolutional Neural Networks with Pytorch

Learn how to implement a Convolutional Neural Network using Pytorch

Learn what is a Convolutional Neural Network

Learn what is Deep Learning

Learn what is a Neural Network

Learn what is convolution

Learn parameters required in convolution

Learn different layers in a convolutional neural network

Learn the mathematics behind the neural networks

Learn how to train a neural network

Learn how to use Pytorch framework to build a Convolutional Neural Network model

Learn how to use Python to build a Deep Learning project

Learn how to use Jupyter notebook to build a Deep Learning project

Learn how to create and train a neural network model with Pytorch

Learn how to analyze a neural network training and measure the performance while training

Learn how to evaluate a neural network model and get metrics to analyze general performance

Learn how to apply changes and exploration analysis in order to improve the neural network model

Basic Python knowledge is required

No previous knowledge in Convolutional Neural Networks is required

No previous knowledge in Deep Learning is required

No previous knowledge in Machine Learning is required

No previous knowledge in Pytorch is required

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 Convolutional Neural Networks and then we will go deeper in the mathematics behind these models 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 convolutional neural network model to classify images into different classes (like classify a picture of a dog as a dog). 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 convolutional 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 your model is. 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 performance.

You don't need previous knowledge on Deep Learning, Convolutional Neural Networks 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 convolutional deep learning projects with this very practical course.

Anyone who wants to learn about how to implement a convolutional neural network using Python and Pytorch

Anyone who wants to learn about how a Deep Learning project is developed



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