Convolutional Neural Networks
Improving a Model
The course is part of these learning paths
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.
- 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
- Data professionals
- People studying for the Google Certified Professional Data Engineer exam
- Introduction to Google Cloud Machine Learning Engine course
- Google Cloud Platform account recommended (sign up for free trial at https://cloud.google.com/free if you don’t have an account)
The GitHub repository for this course is at https://github.com/cloudacademy/ml-engine-doing-more.
Welcome to “Building Convolutional Neural Networks on Google Cloud”. My name’s Guy Hummel and I’ll be showing you how to build more complex neural networks and train then on Machine Learning Engine. I’m the Google Cloud Content Lead at Cloud Academy and I’m a Google Certified Professional Cloud Architect and Data Engineer. If you have any questions, feel free to connect with me on LinkedIn and send me a message, or send an email to email@example.com.
This course is intended for data professionals, especially those who need to design and build big data processing systems. This is an important course to take if you’re studying for the Google Professional Data Engineer exam.
To get the most from this course, you should take the Introduction to Google Cloud Machine Learning Engine course before you take this one, unless you already have plenty of experience with machine learning, TensorFlow, and ML Engine.
Once again, I’ll be showing you how to run examples on ML Engine, so I recommend that if you don’t already have a Google Cloud account, then sign up for a free trial.
In this course, we’re going to work with convolutional neural networks, which have proven to be very useful models for some important machine learning applications.
Next, we’ll look at how to improve a machine learning model. First, we’ll use TensorBoard to visualize how a model is performing. Then, I’ll explain the problem of overfitting and different techniques to prevent it.
Finally, I’ll show you how to scale up your training jobs on ML Engine.
By the end of this course, you should be able to 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, and scale a Cloud ML Engine job using a custom configuration.
We’d love to get your feedback on this course, so please give it a thumbs-up or thumbs-down when you’re finished.
Now, if you’re ready to do more with machine learning on Google Cloud, then let’s get started.
Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).