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Building a Custom Convolutional Neural Network in Keras
In this article, we’ll walk through building a convolutional neural network (CNN) to classify images without relying on pre-trained models. There are a number of popular pre-trained models (e.g. Inception, VGG16, ResNet50) out there that are helpful for overcoming sampling deficiencies; they have already been trained on many images... Read more
Building Neural Networks with Perceptron, One Year Later — Part I
Around one year ago now, I started writing for Open Data Science after presenting neural networks with Perceptron at the ODSC conference. Since then, a lot has changed. People have found fascinating uses for the software, and also help contribute to it. In this series I’ll present a fresh... Read more
Save a Neural Net, Use a Linear Model
Anyone that got their computer science degree in the past five years is probably familiar with this presentation: “We trained a neural network on this dataset over a two-day span to separate our data. Unfortunately, the results weren’t very good; we got around 60 percent accuracy. Next time we’d... Read more
Using Neural Networks, C-RNN-GAN to Generate Music
C-RNN-GAN is a continuous recurrent neural network with adversarial training that contains LSTM cells, therefore it works very well with continuous time series data. For example, music files! Structure of Discriminator and Generator in C-RNN-GAN (picture from original paper) How Does C-RNN-GAN work? C-RNN_GAN is a recurrent neural network with... Read more