Presentation shows how Deep Learning can be used to achieve 99%+ accuracy on handwritten digits using a relatively simple neural network. The network is trained in 2 minutes.

1. DEEP LEARNING: 99%+
ACCURACY FOR
HANDWRITTEN DIGITS
RECOGNITION
JOE ANANDARAJAH

2. PROCESS
Presentation
Focus

3. SEPARATE WITH RADIAL BASIS FUNCTION
• Activation: Tanh
• Learning Rate: 0.1
• Custom Features:
X1
2, X2
2

4. WHAT IF YOUR DATA LOOKS LIKE THIS?
Where are the features?

5. TRAINING & TESTING ENVIRONMENT
• DATA
• MNIST DATASET
• TRAINING DATASET SIZE = 60,000
• TESTING DATASET SIZE = 10,000
• SOFTWARE
• UBUNTU
• PYTHON VIRTUALENV
• TENSORFLOW
• KERAS
• HARDWARE
• INTEL I7
• NVIDIA GEFORCE GTX 1070
• 16GB RAM

6. MULTI-LAYER PERCEPTRON ARCHITECTURE

7. CONVOLUTIONAL NEURAL NETWORK
ARCHITECTURE

8. WHY IS THIS AMAZING?
• TRAIN IN 2 MINS
• RECOGNIZE IN 1000S OF IMAGES PER SECOND
• ERROR RATE OF PRINTED CHARACTERS WOULD ME MUCH LOWER
• US $2,500 DESKTOP
• CHEAPER AND FASTER ON CLOUD
• BEST ERROR RATE WITH CNN 0.23%
• COMPUTERS OUTPERFORM HUMANS IN IMAGE RECOGNITION

9. WHAT NEXT?
• KIDS / BABIES LEARNING DIGITS LEARN A LOT MORE
• LOTS OF WORK NORMALIZING AND LABELING DATASETS
• ACCURACY ON A 4 DIGIT NUMBER = .9934 = 97.2%
• FOR TEXT WE LAYER ON SPELLING AND GRAMMAR CHECKING