Handwritten Devanagari Character Identification

After playing with Cricket vs Baseball images I wanted to try the fastai approach on a more concrete problem where the benchmarks were available. So I chose a dataset with Handwritten Devanagari Character Identification (character set for my mother tongue Marathi)  with SoTA accuracy of 98.47%. The aim was to check if I can beat this number.

I used fastai environment. It is a collection of libraries and lessons created to keep the standard practices/technologies available at one place. The fastai is built on top of the PyTorch, open source machine learning created by Facebook developers. PyTorch is an alternative to TensorFlow and being used widely.

The detailed post with code and output could be found on GitHub. In this post, I am sharing a quick summary.

Steps followed:

1. Import fastai and other required libraries
2. Set config (Path to your data, the size that the images will be resized to)
3. Use a variants of a pre-trained NeuralNet architecture called ResNet
4. Train our data and evaluate the model on the validation set
5. Explore the results

Dataset Samples

The following screenshot displays top incorrect predictions. Some of these are hard to identify for a native Devanagari speaker and writer like me.

 

Deep Learning for Image Classification: Baseball-player vs Cricket-player

If you are from a country which does not play Cricket or Baseball, it could be hard to differentiate. So I tried to build a simple model just to do that.

This post is based on Jeremy Howard's original work of classifying "Cats vs Dogs". I decided to use the same architecture to build the neural network on a different and perhaps more exciting problem statement.

I created a model to label images based on whether they contain a Batter (label=Baseball) or a Batsman (label=Cricket). The aim was to build a basic model with relatively simple images. No exclusive pictures of pitchers, catchers, bowlers, fielders etc.

I used fastai environment. It is a collection of libraries and lessons created to keep the standard practices/technologies available at one place. The fastai is built on top of the PyTorch, open source machine learning created by Facebook developers. PyTorch is an alternative to TensorFlow and being used widely.

The detailed post with code and output could be found on GitHub. In this post, I am sharing a quick summary.

Deep Learning: What is that?

What is it?

"Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence." [1]

What does that mean?

"Teaching machines to think has been a dream/nightmare of scientists for a long time. Rather than teaching a machine explicitly, Deep Learning uses simpler, core ideas and then builds upon them — much as a baby learns sounds, then words, then sentences." [2] 

So just another technology?