Deep learning algorithms require a huge amount of training data. This makes us put more and more labeled data into our training set even if it does not belong to the same distribution of data we are actually interested in. For example, let's say we are building a cat classifier for door camera devices. We … Continue reading What to do when we have mismatched training and validation set?
Earlier, I was of the opinion that getting computers to recognize images requires - huge amount of data, carefully experimented neural network architectures and lots of coding. But, after taking the deep learning course - fast.ai, I found out that it is not always true. We can achieve a lot by writing just a few lines … Continue reading Hotdog or Not Hotdog – Image Classification in Python using fastai
