Run the following code on Deep Learning for genomics on the Google Collaboratory:

https://colab.research.google.com/github/TankMermaid/1000-genomes-genetic-maps/blob/master/A_Primer_on_Deep_Learning_in_Genomics_Public.ipynb

https://colab.research.google.com/github/TankMermaid/1000-genomes-genetic-maps/blob/master, A Primer_on_Deep_Learning_in_Genomics_Public.ipynb a. Describe in a couple of sentences the overall function of this neural network for bioinformatics predictions what the predictions taking place, what are the data used, and what type of neural network we are using ? From which parts of the code you can find the answers to each of these points ? b. How many prediction classes this neural network has, and describe what are these classes. In addition to finding this from the text cells in the code, please also point the parts of the actual code that would demonstrate the number of prediction classes (it should be one of the final layers in the network). c. What portion of the data we use for training, validation and testing ? Where do you see that in the code ? d. Run the code in your Google Collaboratory up to the point where we have the model lost / accuracy graphs (including printing these graphs). What do you observe in these graphs if you modify the testing and validation portions of the datasets ? You would need to re-run the cells from all the way up (where we define the training / validation portions) up and including the cells generating the graphs. Similarly if you reduce significantly the number of epochs, what do you observe in those graphs