Using Keras to predict customer churn based on the IBM Watson Telco Customer Churn dataset. We also demonstrate using the lime package to help explain which features drive individual model predictions. In addition, we use three new packages to assist with Machine Learning: recipes for preprocessing, rsample for sampling data and yardstick for model metrics.
Jeff has worked in open source for the past 20 years and is currently the head of IBM’s open source program office. Needless to say, he has seen firsthand how open source has evolved. In this episode, Jeff covers the four waves of evolution for open source software and talks about open source business models and how IBM is monetizing open source today.
Table of Contents What Is Keras Core? Configuring Your Development Environment Let’s Talk about Keras! Going Beyond with Keras Core The Power of Keras Core: Expanding Your Deep Learning Horizons Show Me Some Code JAX Harnessing model.fit() Imports and Setup…
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With the rapid advance in NLP models we have outpaced out ability to measure just how good they are at human level language tasks. We need better NLP datasets now more than ever to both evaluate how good these models are and to be able to tweak them for out own business domains.
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.
Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 3) Configuring Your Development Environment Need Help Configuring Your Development Environment? Project Structure Implementing CycleGAN Training Implementing Training Callback Implementing Data Pipeline and Model Training Perform Image-to-Image Translation Summary Citation Information CycleGAN:…
The post CycleGAN: Unpaired Image-to-Image Translation (Part 3) appeared first on PyImageSearch.
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