Unit 4 Sprint 3: Major Neural Network Architectures

This week we will review several popular feed-forward neural network architectures that are common in commercial applications.

Module 1: RNNs & LSTMs
- Objectives:
  1. Describe recurrent neural network architecture
  2. Use an LSTM to generate text based on some input
Module 2: CNNs
- Objectives:
  1. Describe convolutions and convolutions within neural networks
  2. Apply pre-trained CNNs to image classification problems
Module 3: Autoencoders
- Objectives:
  1. Describe the componenets of an autoencoder
  2. Train an autoencoder
  3. Apply an autoencoder to a basic information retreval problem
Module 4: LSTMs for Time Series Forecasting
- Objectives:
  1. Understand how to prepare time series data for model ingestion (sliding train-test split)
  2. Understand how to use a LSTM model for time series forecasting applications
  3. Understand the importance of seasonality and trends in time series applications.

Hello world testing