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Combining Dask and PyTorch for Better, Faster Transfer Learning

PyTorch has the ability to train models across multiple machines, and thanks to the framework Dask you can easily create a GPU cluster …

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Handy Dandy Guide to Working With Timestamps in pandas

Using times in pandas can sometimes be tricky--this blog post covers the most common problems.

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Computer Vision at Scale With Dask and PyTorch

This tutorial walks through how to use PyTorch and Dask to train an image recognition model across a GPU cluster.

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Cross-Entropy Loss Function

When working on a Machine Learning or a Deep Learning Problem, loss/cost functions are used to optimize the model during training. The …

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Random Forest on GPUs: 2000x Faster than Apache Spark

This blog post compares using RAPIDS and Dask vs Apache Spark for model training

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Supercharging Hyperparameter Tuning with Dask

The distributed computing framework Dask is great for hyperparameter tuning, since you can train different parameter sets concurrently.

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Practical Issues Setting up Kubernetes for Data Science on AWS

Data science has unique workflows that don't always match those of software engineers and require special setup for Kubernetes.

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Setting Up Your Data Science & Machine Learning Capability in Python

Python is a great language to base your DS/ML framework on, and allows you to avoid being locked into one vendor specific framework.

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