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 consists of 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute. The spectrograms will be treated 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. The model could be optimized using hyperparameter tuning. However, the goal of this notebook is not to build a high performing classifier, rather to show the basic steps to build an image classifier using convolutional neural network. The readers can also get the idea of
https://www.kaggle.com/zubairsamo/setidataset
Type below command in cmd to get up and running with the dependencies of the file.
pip install -r requirement.txt
git clone https://github.com/zubairsamo/Outer_Space_Radio_Signals-Classification .git
Outer_Space_Radio_Signals-Classification .ipynb
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Copyright (c) 2020 Zubair Samo