Sentiment Classification & How To "Frame Problems" for a Neural Network

by Andrew Trask

What You Should Already Know

  • neural networks, forward and back-propagation
  • stochastic gradient descent
  • mean squared error
  • and train/test splits

Where to Get Help if You Need it

  • Re-watch previous Udacity Lectures
  • Leverage the recommended Course Reading Material - Grokking Deep Learning (40% Off: traskud17)
  • Shoot me a tweet @iamtrask

Tutorial Outline:

  • Intro: The Importance of "Framing a Problem"
  • Curate a Dataset
  • Developing a "Predictive Theory"
  • PROJECT 1: Quick Theory Validation
  • Transforming Text to Numbers
  • PROJECT 2: Creating the Input/Output Data
  • Putting it all together in a Neural Network
  • PROJECT 3: Building our Neural Network
  • Understanding Neural Noise
  • PROJECT 4: Making Learning Faster by Reducing Noise
  • Analyzing Inefficiencies in our Network
  • PROJECT 5: Making our Network Train and Run Faster
  • Further Noise Reduction
  • PROJECT 6: Reducing Noise by Strategically Reducing the Vocabulary
  • Analysis: What's going on in the weights?