This repository has a quick overview of the most common Deep Learning applications in a jupyter file. It shows with two different datasets the architecture a VGG network (Convolutional layers) and a GRU network for a time series forecating example. The datasets are CIFAR10 and a dataset with the currency exchange rate of COP and USD. Also it illustrates the most basic aspects of how to define a DL model and it should in the end allow the reader to understand the basic operations of creating, training, saving and loading a model.
You may use the code in this workshop as a guidance and without restrictions. I recomend downloading the whole folder that already contains the datasets used in the workshop with the appropiate paths, that way is an integrated project in itself.
Have fun with it and happy coding! :)