April 15, 2018

Enhancing LSTMs with character embeddings for Named entity recognition

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. We used the LSTM on word level and applied word embeddings. While this approach is straight forward and often yields strong results there are some potential shortcomings. If we haven’t seen a word a prediction time, we have to encode it as unknown and have to infer it’s meaning by it’s surrounding words. Read more

February 3, 2018

How to build a smart product: Transfer Learning for Dog Breed Identification with keras

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. In the following posts I will first show you how to build the bot app with telegram and then how to deploy the app on AWS. Read more

November 27, 2017

Sequence tagging with LSTM-CRFs

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. Now we use a hybrid approach combining a bidirectional LSTM model and a CRF model. This is a state-of-the-art approach to named entity recognition. Let’s recall the situation from the article about conditional random fields. Read more

October 22, 2017

Guide to sequence tagging with neural networks

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. This time we use a LSTM model to do the tagging. At the end of this guide, you will know how to use neural networks to tag sequences of words. For more details on neural nets and LSTM in particular, I suggest to read this excellent post. Read more

August 12, 2017

Classifying genres of movies by looking at the poster - A neural approach

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.misc import matplotlib %matplotlib inline import matplotlib.pyplot as plt …and then we import the movie metadata. path = 'posters/' data = pd.read_csv("MovieGenre.csv", encoding="ISO-8859-1") Now have a look at it. Read more

August 11, 2017

Guide to multi-class multi-label classification with neural networks in python

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. Both of these tasks are well tackled by neural networks. A famous python framework for working with neural networks is keras. We will discuss how to use keras to solve this problem. Read more

August 1, 2017

Efficient AWS usage for deep learning

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.xlarge. I will assume you have an AWS account (or that you are able to get one, it’s easy). Then I can show you how to efficiently use AWS to do deep learning. Read more

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