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Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches
In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.
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Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search
Another simple yet powerful technique we can pair with pipelines to improve performance is grid search, which attempts to optimize model hyperparameter combinations.
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NIPS 2017 Key Points & Summary Notes
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
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 Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018
By Matthew Mayo, KDnuggets Managing Editor on December 15, 2017 in 2018 Predictions, AI, Ajit Jaokar, Brandon Rohrer, Daniel Tunkelang, Hugo Larochelle, Machine Learning, Pedro Domingos, Sebastian Raschka, Xavier AmatriainAs we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2017 and their 2018 key trend predictions.
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Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction
Scikit-learn's Pipeline class is designed as a manageable way to apply a series of data transformations followed by the application of an estimator.
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Big Data: Main Developments in 2017 and Key Trends in 2018
By Matthew Mayo, KDnuggets Managing Editor on December 5, 2017 in 2018 Predictions, Big Data, Bill Inmon, Bill Schmarzo, Doug Laney, James Kobielus, Matei Zaharia, Meta Brown, Predictions, Ronald van Loon, Trends, Yves MulkersAs we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.
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Using Deep Learning to Solve Real World Problems
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
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A General Approach to Preprocessing Text Data
Recently we had a look at a framework for textual data science tasks in their totality. Now we focus on putting together a generalized approach to attacking text data preprocessing, regardless of the specific textual data science task you have in mind.
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Building a Wikipedia Text Corpus for Natural Language Processing
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
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A Framework for Approaching Textual Data Science Tasks
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
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Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018
Big Data: Main Developments in 2017 and Key Trends in 2018
A Framework for Approaching Textual Data Science Tasks