Classification with Transfer Learning in Keras

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在此指导项目中,您将:

How to implement transfer learning with Keras and TensorFlow

How to use transfer learning to solve image classification

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training. In order to be successful in this project, you should be familiar with Python, Neural Networks, and CNNs. 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.

您要培养的技能

  • Deep Learning
  • Inductive Transfer
  • Convolutional Neural Network
  • Machine Learning
  • Tensorflow

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Import Libraries and Helper functions

  2. Download the Pet dataset and extract relevant annotations

  3. Add functionality to create a random batch of examples and labels

  4. Create a new model with MobileNet v2 and a new fully connected top layer

  5. Create a data generator function and calculate training and validation steps

  6. Get predictions on a test batch and display the test batch along with prediction

指导项目工作原理

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在分屏视频中,您的授课教师会为您提供分步指导

授课教师

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