Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand.

Building machine learning models can be a time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering.

These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.

Forecasting within automated machine learning (ML) takes these factors into consideration and includes capabilities that improve the accuracy and performance of our recommended models.

This session will highlight the forecasting features of Automated ML and how to leverage them.

Time index:

  • [00:35] – What is time-series forecasting?
  • [01:30] – Simplify ML with Automated ML
  • [02:30] – DriveTime customer scenario
  • [04:15] – Features & Functionality
  • [05:20] – Demo

Learn More:

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.NET for Apache Spark empowers developers with .NET experience or code bases to participate in the world of big data analytics.

In this episode, Brigit Murtaugh joins Rich to show us how to start processing data with .NET for Apache Spark.

Time index:

  • [01:01] – What is Apache Spark?
  • [02:33] – What are customers using Apache Spark for?
  • [03:50] – What did we create .NET for Apache Spark?
  • [06:30] – Exploring GitHub data
  • [15:012] – Considering data processing in the real world
  • [18:26] – Analyzing continuous data streams

Useful Links

GANs (Generative Adversarial Networks) are a set of deep neural network models used to produce synthetic data.

The method was developed by Ian Goodfellow in 2014 and is outlined in the paper Generative Adversarial Networks.

The goal of a GAN is to train a discriminator to be able to distinguish between real and fake data while simultaneously training a generator to produce synthetic instances of data that can reliably trick the discriminator.

Here’s a great tutorial on how to create GANs in Python.

A popular application of GANs was in the ‘GANgough’ project where synthetic paintings were generated by GANs trained on paintings from wikiart.org. The independent researchers, Kenny Jones and Derrick Bonafilia, were able to generate synthetic religious, landscape, flower and portrait images with impressive performance. The article GANGough: Creating Art with GANs details the method. In this post, we will walk through the process of building a basic GAN in python which we will use to generate synthetic images of handwritten digits.