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?