Making Sense of Big Data

Making convnets “chill”

See the github link here: Image by Author (screenshotted from github link)


Photo by John Thomas on Unsplash. This bear has absolutely nothing to do with the content of the article. I Just like bears. Bears, beats, battlestar galactica.

Applied Machine Learning

Using C++ and CUDA Extensions to write high performance kernels in PyTorch


Photo by Jeffrey Wegrzyn on Unsplash

Applying Kalman Filters to real-time streaming data

  1. Tracking the actual values for highly sensitive sensors
  2. Tracking the velocity of objects with uncertainty


Photo by Isaac Smith on Unsplash


Identify the trend in univariate time series data using statistics!

Photo by Joshua Sukoff on Unsplash

A visual method for exploring natural clusters in transcribed speeches

  1. Sentence Embeddings to create homogenous statement representations
  2. K-Means to cluster statements
  3. T-SNE for dimensionality reduction

Photo by John Schnobrich on Unsplash

Getting Started, The Business of Data Science

A summary of how to get it done: taking ideas and putting them into production

Photo by Brett Jordan on Unsplash

Using information theory on natural language

“Yeah, mmm-hmm, that’s interesting”


Photo by Kelly Sikkema on Unsplash

A short primer on scaling up your deep learning to multiple GPUs


  1. Package/restructure your application (see github repo here for an example)
  2. Create a docker image and load that image to Google’s Cloud Registry
  3. Create an instance and run your training job

Photo by ThisisEngineering RAEng on Unsplash

A short primer on scaling up your deep learning to multiple GPUs

Part 1 — Setting up the experiment and laying the foundation

Photo by Henry & Co. on Unsplash


A tensor’s journey through an LSTM Layer visualized

Sam Black

Data Scientist

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