Completed Jupyter Notebook with SETI Data.
Build and train a convolutional neural network (CNN) using Keras. Display results and plot 2D spectrograms with Python in Jupyter Notebook.
In this project, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve an image classification problem. The data we are going to use consists of 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute. We will treat the spectrograms as images to train an image classification model to classify the signals into one of four classes. By the end of the project, you will have built and trained a convolutional neural network from scratch using Keras to classify signals from space.
SKILLS YOU WILL DEVELOP Deep Learning, Convolutional Neural Network, Machine Learning, Tensorflow, keras