Building Convolutional Neural Networks on Google Cloud

Once you know how to build and train neural networks using TensorFlow and Google Cloud Machine Learning Engine, what’s next? Before long, you’ll discover that prebuilt estimators and default configurations will only get you so far. To optimize your models, you may need to create your own estimators, try different techniques to reduce overfitting, and use custom clusters to train your models.

Convolutional Neural Networks (CNNs) are very good at certain tasks, especially recognizing objects in pictures and videos. In fact, they’re one of the technologies powering self-driving cars. In this course, you’ll follow hands-on examples to build a CNN, train it using a custom scale tier on Machine Learning Engine, and visualize its performance. You’ll also learn how to recognize overfitting and apply different methods to avoid it.

Learning Objectives

Build a Convolutional Neural Network in TensorFlow
Analyze a model’s training performance using TensorBoard
Identify cases of overfitting and apply techniques to prevent it
Scale a Cloud ML Engine job using a custom configuration

Intended Audience

Data professionals
People studying for the Google Certified Professional Data Engineer exam

The github repository for this course is at https://github.com/cloudacademy/ml-engine-doing-more.