Mark-McAdam/DS-Unit-4-Sprint-3

DS-Unit-4-Sprint-3

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: Artificial General Intelligence & the Future
  • Objectives:
    1. Describe the history of artificial intelligence research
    2. Know the important research achievements in AI
    3. Delineate the ethnical challenges faces AI

Hello world testing

mkl-fft==1.0.12 1.1.0 mkl-random==1.0.2 1.1.1 mkl-service==2.0.2 2.3.0

1.1.0 conda install -c conda-forge mkl_fft

1.1.1 conda install -c conda-forge mkl_random

2.3.0 conda install -c anaconda mkl-service