Deep-Learning-CNN-Case-study-

3 Case study based on CNN using Keras deep learning.

1) IMDB

.Classifying movie reviews: a binary classification example

Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. In this example, you’ll learn to classify movie reviews as positive or negative, based on the text content of the reviews.

2) The Boston Housing Price dataset

• You’ll attempt to predict the median price of homes in a given Boston suburb in the mid-1970s, given data points about the suburb at the time, such as the crime rate, the local property tax rate, and so on.
• It has relatively few data points: only 506, split between 404 training samples and 102 test samples. And each feature in the input data (for example, the crime rate) has a different scale.
• For instance, some values are proportions, which take values between 0 and 1; others take values between 1 and 12, others between 0 and 100, and so on.

3) The Reuters dataset

a multiclass classification example

You’ll work with the Reuters dataset, a set of short newswires and their topics, published by Reuters in 1986. It’s a simple, widely used toy dataset for text classification. There are 46 different topics; some topics are more represented than others, but each topic has at least 10 examples in the training set.

Like IMDB and MNIST, the Reuters dataset comes packaged as part of Keras. Let’s take a look.