Space Science with Python - Asteroids meet Deep Learning #10
Hey everyone,

today I'd like to show you how to optimize a Conv1D network using [Keras-Tuner](https://keras.io/keras_tuner/). It enables one to automatically test some pre-defined networks; or it applies Bayesian or Hyperband optimization to find the best model!

In our case, we'll use it to find a good convolutional neural network for asteroid spectra to distinguish between 4 classes.

Link to video: [https://youtu.be/vhr48KgL-Ys](https://youtu.be/vhr48KgL-Ys)

Corresponding Code: [https://github.com/ThomasAlbin/Astroniz-YT-Tutorials/blob/main/%5BML1%5D-Asteroid-Spectra/10\_dl\_hyperparameter\_search.ipynb](https://github.com/ThomasAlbin/Astroniz-YT-Tutorials/blob/main/%5BML1%5D-Asteroid-Spectra/10_dl_hyperparameter_search.ipynb)

Please note that this is tutorial #10 in a long series of space science related videos. Feel free to take a look in the corresponding [playlist](https://www.youtube.com/playlist?list=PLNvIBWkEdZ2gagAcgm44cplgSvQ_Cmvbv) or on [my channel](https://www.youtube.com/c/Astroniz).

Tutorial #10 concludes the classification task. In the next 3 videos we'll take a look at Autoencoders and how to classify or identify different asteroid spectra in a low-dimensional latent space.

Stay tuned!

Thomas