This is the second article of exploring DL4J library to learn deep learning concepts. In this we’ll work with our first image classification problem.
It is of high importance that you finish this tutorial first:
We’ll be using a famous dataset called MNIST (Basically the hello world of image classification). General MNIST dataset consist of 70,000 images of 28×28 pixels, representing handwritten 0–9 digits. 60,000 are part of the training set, which is the set used to train the network, while the remaining 10,000 are part of the test set. Download the .zip file:
If you extract it (for now jst extract it in downloads folder as we’re exploring) you can see the dataset is split into two folders: training and testing, each one containing 10 subfolders, labeled 0 to 9, each one in turn containing thousands (almost 6,000) of image samples of handwritten digits correspondent to the label identified by the subfolder name. …
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A picture speaks a thousand words:
Recommendation engine in its simplest form of understanding is what you see in the picture above and for this getting started guide it’s enough.
Customers can be similar when they are from same city, or go to same school or are in the same age bracket or there can be many other factors. …
This is the first tutorial of a series of tutorials I’ll be writing in which you’ll work on building Neural Networks using DL4J (A Java-based deep learning library).
The only prerequisite is the knowledge of Java. If you have worked with basic Java SE and understand the basic Object-Oriented Programming (OOP) concepts you’ll be good to go. Also, a basic understanding of Neural Network or deep learning and the concepts would be a big plus.
We’ll be using IntelliJ IDEA CE. Download the Community version:
Open IntelliJ IDEA CE and Create a new project, name it LearningDL4J.
Add the following code in the