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The AWS SageMaker ntm_20newsgroups_topic_model example notebook is a simple to follow introduction to SageMaker’s pre-packaged Natural Language Processing (NLP) tools. The notebook demonstrates how to use the Neural Topic Model (NTM) algorithm to extract a set of topics from a sample usenet newsgroups dataset and visualize as word clouds. It also contains code demonstrating how […]
This question came up recently on a project where Pandas data needed to be fed to a TensorFlow classifier. In this case, we wanted to divide the dataframe using a random sampling. Frameworks like scikit-learn may have utilities to split data sets into training, test and cross-validation sets. For example, sklearn.model_selection.train_test_split split numpy arrays or […]
Here are my best reasons for using container solutions like Docker: 1. Simplify the deployment process! I used to struggle with software deployments. Traditional software package installers work fine for simple deployments on a single platform type, but require maintenance if deployment guidelines change between OS verisons. Deployment tools that need to target multiple platforms […]
All python code is Python 3.5+. Having an automatic way to build GitHub pull requests before merging saves a lot of time and trouble compared with pulling, building and testing a GitHub pull request locally. TeamCity makes it easy to set this up using branch specifications. The blog post refers to a much older version […]
All python code is Python 3.5+. PEP484 goes beyond built-in type annotations. Another feature of the Python type hinting libary is the ability to create type aliases. I’ve used type aliasing frequently in C++ (typedef, using) to improve code readability and for its other benefits. I’m happy to see that it’s available in Python too. […]
All python code in this post is Python 3.5+. In my previous post, I described how I got usable Pandas dataframes from the Kaggle movies dataset. My next step was to start exploring the data with simple visualizations. The first feature I wanted explore was the distribution of movies by year in the movies_metadata data […]
All python code in this post is Python 3.5+. This post describes how I parsed movies_metadata.csv from the Kaggle movies dataset; a task I started in Part 1 and Part 2. After some digging into the Pandas documentation and Stack Overflow, I found that the best solution to my parsing problems was to explicitly set […]
All python code is Python 3.5+. In the Pandas data import posts, I’m using Python type hints. Type hinting is a fairly new feature in Python and has been provisionally accepted as a language feature. It is also a thought-provoking design feature for a dynamically typed language. Types are not enforced at runtime. The type […]
All python code in this post is Python 3.5+. Continuing from Part 1, I discovered that movies_metadata.csv contains malformed rows that have missing fields, which is what caused file import to fail. I tried experimenting with some of the more advanced Pandas.read_csv parameters to see if I could work around the malformed rows. def main(path: […]
All python code in this post is Python 3.5+. I’m continuing to work with the same Kaggle movies dataset as in the SQL import experiment. This time, I imported the data into Pandas DataFrames. The trickiest dataset to import was movies_metadata.csv. I first tried to use pandas.read_csv with the default settings. import argparse import pandas […]