In this course, we will take a look at different strategies for building an image classifier using convolutional neural networks. We’ll improve the model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

Table of Contents

• Welcome to Image Understanding with TensorFlow on GCP
  18mins
• Linear and DNN Models
  65mins
• Convolutional Neural Networks (CNNs)
  37mins
• Dealing with Data Scarcity
  35mins
• Going Deeper Faster
  64mins
• Pre-built ML Models for Image Classification
  33mins
• Summary
  3mins