I wanted to classify handwritten digits using core Python without any high level frameworks to essentially understand how a neural network works.
What it does
Learn from the MNIST dataset with Adam optimization algorithm and classify the test digit images.
How I built it
I built the vanilla neural network using python, numpy, pandas, and a lot of patience.
Challenges I ran into
Adam was extremely hard to implement! It took a lot of debugging of the inter-modular variables and dictionaries to get it to work.
Accomplishments that I'm proud of
I scored 99% accuracy on training set and 95% accuracy on test set. A good learning curve for me!
What I learned
Adam, deep learning, neural networks, patience.
What's next for Digit Recognizer
Also implement Adagrad on the dataset and graph the costs of different optimization algorithms together.
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