Deploying Prediction Models

  AI for Microplastic Images Classification   I want to present our final project for the Diploma in Advanced Artificial Intelligence offered by Samsung Innovation Campus and Universidad de Málaga . We decided to create our team under the name "Minerva Dev Team". We chose this name because we admire the figure of Minerva, goddess of wisdom, the arts, education, and justice. It seemed to us that this name best defined a team of five women, each so different yet contributing the best of themselves to create something beautiful from this experience. The project is called PlastiScan AI, a model designed, trained, and optimized with our own convolutional neural network. PlastiScan is responsible for analyzing images of water samples for the detection and classification of microplastic particles in them. Our purpose is to create a tool for research into such an important and current environmental issue, with the aim of helping to control pollution sources, understandin...

  Stock Price Prediction (Apple) with SimpleRNN Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language. Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has seen so far. In this exercise I'm going to use  keras.layers.SimpleRNN , a fully-connected RNN where the output from previous timestep is to be fed to next timestep. By default, the output of a RNN layer contains a single vector per sample. This vector is the RNN cell output corresponding to the last timestep, containing information about the entire input sequence. The shape of this output is (batch_size, units) where units corresponds to the units argument passed to the layer's constructor. A RNN layer can also return the entire sequence of outputs for each sample (one vector per timestep per sample), if...