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Formal programming is very much a part of Deep Learning and we show this is an accurate implementation of ML for our example.

Welcome to the roll out of the fantastic Deep Learning tutorial. Not so long ago I wrote a post called “Getting started with R(lab) and JVM(lab) together”. With the recent launch of the synthetic data movers package we added speed (Shala+) and an awesome small library named “Cacast” that allows you to build, run, split and program experimental R/JVM running on most distributions of Kubeflow and HashiFlow. This example is just another way to introduce you to how fast and efficient it can be to speed-run some experiments.

In general we will talk about how easy it is to speed-run experiments on Python, but of course it can be done with regular R for cloud experiments which would not be finished in practical underrun time.

Downloading the finished images for each experiment

We can download raw images from GitHub into a vanilla .zip file and can be csv format. Get the latest jpeg images from the web by double-checking the link at https://flickr.com/photos/103913523914/albums. Simply make sure to download the Jpeg file of the top 20% highest quality. This is also the best way to get artist's images on Raspberry Pi (same ones provided in instructions).

Loading images and rearranging them

After downloading you should have the following files:

folder : We will need to name the folder after we upload images to make the main folder. The mlbzip command will always return a jpeg file.

, we will need to name the folder after we upload images to make the main folder. The mlbzip command will always return a jpeg file. Uploading : The only process we need to do now is to upload the raw images. When we do, save the images, add the below command to the "Downloads" folder:

The images can be mounted as .json (it will ask the initial setup if it really needs output files. We already have them from the command above) or the lines that appear when we run them on RStudio using the .gitignore command command.

The images can be mounted as .json (it will ask the initial setup if it really needs output files. We already have them from the command above) or the lines that appear when we run them on RStudio using the .gitignore command. Made: When it is time to run the experiment, I recommend running the following command:

After running this command the commands have been executed and the Ubuntu images are ready for cloning.

That brings us to the first process. I would suggest writing a strong path using variables I added later in our plot.

This opens on the command line:

the commands have been executed and the Ubuntu images are ready for cloning. keep and delete an variables as per our code you may write the following:

The commands have been executed and the Ubuntu images are ready for cloning. If you want to add, keep and delete an variables as per our code you may write the following: