DS-Unit-4-Sprint-3-Deep-Learning

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DS-Unit-4-Sprint-3-Deep-Learning

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DS-Unit-4-Sprint-3-Deep-Learning 최신버젼 다운로드

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# 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. 
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* 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.

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DS-Unit-4-Sprint-3-Deep-Learning

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