Summary

From the closing price of the stock market to the number of clicks per second on a web page or the sequence of venues visited by a tourist exploring a new city, time series and temporal sequences of discrete events are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations.

Join Bruno Gonçalves to learn how to use recurrent neural networks, a technique originally developed for natural language processing, to model and forecast time series. You’ll also discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

Program

  • How the brain works

  • Optimization

  • Artificial Neurons

  • Activation Functions

  • Backpropagation

  • Loss Function

  • Feed Forward Networks

  • Recurrent Neural Network

  • Long-Short Term Memory

  • Gated Recurrent Unit

  • Keras API

Resources

Previous Editions: