1. A Kaggle Project By Ryan Bambrough
Digit Recognizer
A Kaggle Project
By Ryan Bambrough

2. Problem To Solve
Given a 28 x 28 pixel image determine what digit is present
Data includes 42,000 training samples & 28,000 testing samples
Used for a multitude of applications:
Recognizing address on letters
Digital ink to text

3. Approaches
This is an ‘older’ Neural Network problem with a large dataset (MNIST)
Quite a few ways to solve it, each with its own benefits and complexities
Can add a lot of optimizations to get a little more accuracy
Law of diminishing returns

4. Chosen Approach I chose to go with a simple Multi-Layer Network
Basic Neural Network, no bells or whistles
Sigmoidal Activation Function
784 Inputs Notes (28 x 28)
30 – 100 Hidden Nodes
10 Output Nodes (One for each possible output)

5. Problems Encountered Setting up useful python environment on Windows
Had used python before, just not for neural networks
Pre-Processing data, while not terrible difficult took time to ensure accuracy

6. Quality
The accuracy of the network depended greatly on its composition:
30 Hidden, 2.75 Eta, 60 Epochs, 20 Smp/Epoch ~94.77 %
100 Hidden, 2.75 Eta, 60 Epochs, 20 Smp/Epoch ~86.99 %
30 Hidden, 5.00 Eta, 60 Epochs, 20 Smp/Epoch ~12.59 %
30 Hidden, 5.00 Eta, 60 Epochs, 50 Smp/Epoch ~94.39 %

7. Conclusions
For this Kaggle competition it is fairly easy to get a decent accuracy with a simple network
Even simple networks, if implemented with nicely pre-processed data, can perform extremely well and highly efficient

8. References Professor Hu’s Lecture Slides
Using neural nets to recognize handwritten digits, by Michael Nielsen,