From Machine Learning Bookcamp by Alexey Grigorev
In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.
From Machine Learning with TensorFlow, Second Edition by Chris Mattmann This article covers: Building sentiment classifier using logistic regression and with softmax Measuring classification accuracy Computing ROC curve and measure classifier effectiveness Submitting your results to the Kaggle challenge for… Continue Reading →
From Machine Learning with TensorFlow, Second Edition by Chris Mattmann
This article covers using text and word frequency (Bag of Words) to represent sentiment.
From Ensemble Methods for Machine Learning by Gautam Kunapuli
Our first case study explores a medical decision-making task: breast cancer diagnosis. We will see how to use scikit-learn’s homogeneous parallel ensemble modules in practice. Specifically, we will train and evaluate the performance of three homogeneous parallel algorithms, each characterized by increasing randomness: bagging with decision trees, random forests and ExtraTrees.
From The Well-Grounded Python Developer by Doug Farrell
This article delves into using the OOP coding paradigm in the Python language.
The Cool Way to Search Text
By Scott Penbertht and Chris Mattmann
| Andrew Ferlitsch reveals new paradigms–and patterns–for automated deep learning
Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components. |
From Interpretable AI by Ajay Thampi
