Fashion Image Classification using CNNs in Pytorch

ofrecido por
Coursera Project Network
En este Proyecto guiado, tú:

Learn How to use Pytorch to create Neural Network Models.

Learn How to build and train Convolutional Neural Networks in Pytorch.

Clock2 hours
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

In this 1-hour long project-based course, you will learn how to create Neural Networks in the Deep Learning Framework PyTorch. We will creating a Convolutional Neural Network for a 10 Class Image Classification problem which can be extended to more classes. We will start off by looking at how perform data preparation and Augmentation in Pytorch. We will be building a Neural Network in Pytorch. We will add the Convolutional Layers as well as Linear Layers. We will then look at how to add optimizer and train the model. Finally, we will test and evaluate our model on test data. The project will get you introduced with Pytorch. You will in the end understand how the framework works and get you started with building Neural Networks in Pytorch. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás

Convolutional Neural NetworkDeep Learningpytorchimage classification

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction to the Task, Google Colab, CNNs, Pytorch

  2. Setting up Data preparation & Augmentation using Transforms

  3. Importing & Loading Data

  4. Building the Convolutional Neural Network

  5. Training the Neural Network Model

  6. Testing and Evaluating the Model

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Preguntas Frecuentes

Preguntas Frecuentes

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