• Online, Self-Paced
Course Description

In this 19-video course, learners explore deep learning with Keras, including how to create and use neural networks with Keras for machine learning solutions. Begin with an overview of what neural networks are and their main components, followed by an introduction to Keras and its guiding principles. Observe how to configure Microsoft Cognitive Toolkit (CNTK) as your Keras backend; install and configure Keras; identify and work with both types of models available in Keras; and recognize features of commonly-used Keras layers and when to use them. Use Keras to make regression classifications and image classifications; Keras metrics to judge a model's performance; and Jupyter Notebooks with Keras. Next, download and load a data set from MNIST or CIFAR-10; explore data sets in Keras; prepare your data in Keras by defining input and target tensors, and compile the model in Keras. Then train and test your neural network; evaluate and score the performance of neural networks in Keras, and make predictions using your data set in Keras. The closing exercise involves using a neural network to make predictions.

Learning Objectives

In this 19-video course, learners explore deep learning with Keras, including how to create and use neural networks with Keras for machine learning solutions. Begin with an overview of what neural networks are and their main components, followed by an introduction to Keras and its guiding principles. Observe how to configure Microsoft Cognitive Toolkit (CNTK) as your Keras backend; install and configure Keras; identify and work with both types of models available in Keras; and recognize features of commonly-used Keras layers and when to use them. Use Keras to make regression classifications and image classifications; Keras metrics to judge a model's performance; and Jupyter Notebooks with Keras. Next, download and load a data set from MNIST or CIFAR-10; explore data sets in Keras; prepare your data in Keras by defining input and target tensors, and compile the model in Keras. Then train and test your neural network; evaluate and score the performance of neural networks in Keras, and make predictions using your data set in Keras. The closing exercise involves using a neural network to make predictions.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture