This is the fifth post in my series about named entity recognition. If you haven’t seen the last four, have a look now. The last time we used a CRF-LSTM to model the sequence structure of our sentences. Read more

This time I will show you how to build a simple “AI” product with transfer learning. We will build a “dog breed identification chat bot”. In this first post, I will show how to build a good model using keras, augmentation, pre-trained models for transfer learning and fine-tuning. Read more

This is the fourth post in my series about named entity recognition. If you haven’t seen the last three, have a look now. The last time we used a recurrent neural network to model the sequence structure of our sentences. Read more

This is the third post in my series about named entity recognition. If you haven’t seen the last two, have a look now. The last time we used a conditional random field to model the sequence structure of our sentences. Read more

In this article, we will apply the concept of multi-label multi-class classification with neural networks from the last post, to classify movie posters by genre. First we import the usual suspects in python. import numpy as np import pandas as pd import glob import scipy. Read more

Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple topics. Read more

When running experiments with deep neural nets you want to use appropriate hardware. Most of the time I work on a thinkpad laptop with no GPU. This makes experimenting painfully slow. A convenient way is to use an AWS instance, for example the p2. Read more