mnist – RCP Vision

Convolutional Neural Networks with Eclipse Deeplearning4j

In the previous article we have seen how to use Eclipse Deeplearning4j for building, training and testing a simple and classic MLP (Multi Layer Perceptron) neural network. As a dataset we used the "hello world" example of deep learning, that is the MNIST: a dataset of 70,000 b/w images of 28×28 pixels, representing handwritten 0-9 digits. Now we will explore a different architecture known as Convolutional Neural Network (CNN), which is very powerful, particularly when dealing with image classification tasks. After having explained how this network works and why it is so efficient, then we will take our previous MLP example, [...]

Build your first neural network with Eclipse Deeplearning4j

In the previous article we had an introduction to deep learning and neural networks. Here we will explore how to design a network depending on the task we want to solve. There is indeed an incredibly high number of parameters and topology choices to deal with when working with neural networks: how many hidden layers should I set up? What activation function should they use? What are good values for the learning rate? Should I use a classical Multilayer Neural Network, a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN) or one of the other architectures available? These questions are [...]