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Pluralsight.Building.Neur▄l.Networks.with.▄cikit-learn-XQZT
Pluralsight.Building.Neural.Networks.with.scikit-learn-XQZT
Building Neural Networks with scikit-learn
310.7 MB
08/20/2019
Course # : N/A
Published : Aug 19 2019
Modified : N/A
URL : www.pluralsight.com/library/courses/building-neural-networks-scikit-learn
Author : Janani Ravi
Duration : 1h 56m
Skill : Advanced
Exer/Code : [X]
Description:
Even as the number of machine learning frameworks and libraries increases on a
daily basis, scikit-learn is retaining its popularity with ease. The one domain
where scikit-learn is distinctly behind competing frameworks is in the
construction of neural networks for deep learning. In this course, Building
Neural Networks with scikit-learn, you will gain the ability to make the best of
the support that scikit-learn does provide for deep learning. First, you will
learn precisely what gaps exist in scikit-learn’s support for neural networks,
as well as how to leverage constructs such as the perceptron and multi-layer
perceptrons that are made available in scikit-learn. Next, you will discover how
perceptrons are just neurons with step activation, and multi-layer perceptrons
are effectively feed-forward neural networks. Then, you'll use scikit-learn
estimator objects for neural networks to build regression and classification
models, working with numeric, text, and image data. Finally, you will use
Restricted Boltzmann Machines to perform dimensionality reduction on data before
feeding it into a machine learning model. When you’re finished with this course,
you will have the skills and knowledge to leverage every bit of support that
scikit-learn currently has to offer for the construction of neural networks.