TensorFlow for CNNs: Data Augmentation

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In diesem angeleitetes Projekt werden Sie:

Learn how to apply Data Augmentation on Images

Learn how to artificially increase the number of training examples

Learn how to create Data Augmentation models with Keras and Tensorflow

Clock2 hours
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 2-hour long project-based course, you will learn In this project, you will learn practically how to build a data augmentation model which is a key topic in training visual recognition systems with real-world applications, and you will create your own data augmentation algorithm with TensorFlow and apply it to real data, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned the fundamentals of data augmentation and created a deep learning model with TensorFlow, and applied data augmentation using real images. This class is for learners who want to learn how to work with convolutional neural networks and use Python for applying data augmentation to images with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training convolutional neural networks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.

Kompetenzen, die Sie erwerben werden

TensorflowConvolutional Neural NetworkArtificial Neural NetworkDeep Learning

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Introduction and overview of the project

  2. Import Libraries and Setup the Dataset

  3. Use Keras Layers for Rescaling, Augmentation

  4. Use Keras for Preprocessing and Training

  5. Use Tensorflow to Apply Image Augmentation

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

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