# Learn to identify ingredients with neural networks

Today we want to build a model, that can identify ingredients in cooking recipes. I use the “German Recipes Dataset”, I recently published on kaggle. We have more than 12000 German recipes and their ingredients list. First we will generate labels for every word in the recipe, if it is an ingredient or not. Then we use a sequence-to-sequence neural network to tag every word. Then we pseudo-label the training set and update the model with the new labels.

# Understanding text data with topic models

This is the first post of my series about understanding text datasets. A lot of the current NLP progress is made in predictive performance. But in practice, you often want and need to know, what is going on in your dataset. You may have labels that are generated from external sources and you have to understand how they relate to your text samples. You need to understand potential sources of leakage.

# Image segmentation with test time augmentation with keras

In the last post, I introduced the U-Net model for segmenting salt depots in seismic images. This time, we will see how to improve the model by data augmentation and especially test time augmentation (TTA). You will learn how to use data augmentation with segmentation masks and what test time augmentation is and how to use it in keras. For convenience we reuse a lot of functions from the last post.

# U-Net for segmenting seismic images with keras

Today I’m going to write about a kaggle competition I started working on recently. In the TGS Salt Identification Challenge, you are asked to segment salt deposits beneath the Earth’s surface. So we are given a set of seismic images that are $101 \times 101$ pixels each and each pixel is classified as either salt or sediment. The goal of the competition is to segment regions that contain salt. A seismic image is produced from imaging the reflection coming from rock boundaries.

# Guide to word vectors with gensim and keras

Word vectors Today, I tell you what word vectors are, how you create them in python and finally how you can use them with neural networks in keras. For a long time, NLP methods use a vectorspace model to represent words. Commonly one-hot encoded vectors are used. This traditional, so called Bag of Words approach is pretty successful for a lot of tasks. Recently, new methods for representing words in a vectorspace have been proposed and yielded big improvements in a lot of different NLP tasks.

# 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.

# A strong and simple baseline to classify toxic comments on wikipedia with keras

This time we’re going to discuss a current machine learning completion on kaggle. In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. You’ll be using a dataset of comments from Wikipedia’s talk page edits. I will show you how to create a strong baseline using python and keras. import pandas as pd import numpy as np import matplotlib.