Regression Math: Using Gradient Descent & Logistic Regression
Math
| Intermediate
- 13 videos | 1h 37m 14s
- Includes Assessment
- Earns a Badge
Gradient descent is an extremely powerful numerical optimization technique widely used to find optimal values of model parameters during the model training phase of machine learning. Use this course as an introduction to gradient descent, examining how it can be used in a wide variety of optimization problems. Explore how it can be used to perform linear regression, carefully studying the matrix equations used to compute the gradients and updating the model parameters using the gradients as well as the learning rate hyperparameter. Finally, apply a form of gradient descent known as stochastic gradient descent to fit an S-curve, thus implementing logistic regression on a data set. By the end of the course, you'll be able to assuredly implement logistic regression using gradient descent.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseoutline how gradient descent worksdescribe what gradients are used forwork through a calculation of an epochstandardize and shape data for gradient descentimplement a single epochperform gradient descent
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recall how logistic regression can be used for classificationcalculate an S-curve in logistic regressionidentify correlations for performing logistic regressionset up training and testing data for logistic regressionexplore and perform stochastic gradient descentsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2mUP NEXT
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2.How Gradient Descent Works7m 41s
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3.What Gradients Are Used For7m 35s
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4.Computing Gradient Descent6m 40s
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5.Setting up Data for Gradient Descent9m 17s
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6.Defining an Epoch Manually9m 35s
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7.Performing Gradient Descent Manually9m 53s
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8.How Logistic Regression Works6m 51s
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9.Computing an S-curve7m 51s
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10.Viewing Correlations for Logistic Regression9m 19s
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11.Splitting and Shaping Data for Logistic Regression8m 21s
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12.Performing Logistic Regression with Gradient Descent10m 1s
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13.Course Summary2m 10s
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