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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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Snowflake and Dask

This article covers efficient ways to load data from Snowflake into a Dask distributed cluster.

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3 Ways to Schedule and Execute Python Jobs

Being able to run a Python script on a schedule is an important part of many data science tasks. This blog post walks through three …

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A Guide to Convolutional Neural Networks — the ELI5 way

Artificial Intelligence has been witnessing monumental growth in bridging the gap between the capabilities of humans and machines. …

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