投稿

4-07. Implementing Stochastic Gradient Descent Classification

173

0

2023-05-20 22:56:10

未经作者授权，禁止转载

正在缓冲...

Building Classification Models with scikit-learn

AI虚拟之声实验室

虚拟之声创作计划·2023第二期

eagleeyecherry 发消息

https://space.bilibili.com/508290323/

1-1. Course Overview

01:43

2-1. Module Overview

01:19

2-2. Prerequisites and Course Outline

01:30

2-4. Logistic Regression Intuition

05:35

2-5. Cross Entropy Intuition

02:04

2-6. Accuracy, Precision, and Recall

06:12

2-7. Determining Decision Threshold Using ROC Curves

06:45

2-8. Types of Classification

03:58

2-9. Module Summary

01:14

3-1. Module Overview

01:04

3-2. Installing and Setting up scikit-learn

03:07

3-3. Exploring the Titanic Dataset

07:20

3-4. Visualizing Relationships in the Data

05:12

3-5. Preprocessing the Data

04:29

3-6. Training a Logistic Regression Binary Classifier

05:12

3-7. Calculating Accuracy, Precision and Recall for the Classification Model

04:54

3-8. Defining Helper Functions to Train and Evaluate Classification Models

07:06

3-9. Module Summary

01:19

4-01. Module Overview

01:24

4-02. Choosing Classification Algorithms

02:03

4-03. Linear Discriminant Analysis and Quadratic Discriminant Analysis

07:29

4-04. Implementing Linear Discriminant Analysis Classification

03:43

4-05. Implementing Quadratic Discriminant Analysis Classification

01:54

4-06. Stochastic Gradient Descent

02:47

4-07. Implementing Stochastic Gradient Descent Classification

02:37

4-08. Support Vector Machines

07:19

4-09. Implementing Support Vector Classification

02:46

4-10. Nearest Neighbors

03:06

4-11. Implementing K-nearest-neighbors Classification

01:27

4-12. Decision Trees

03:01

4-13. Implementing Decision Tree Classification

02:34

4-14. Naive Bayes

04:05

4-15. Implementing Naive Bayes Classification

01:32

4-16. Module Summary

01:42

5-1. Module Overview

01:02

5-2. Hyperparameter Tuning

03:55

5-3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search

06:41

5-4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search

02:35

5-5. Module Summary

01:05

6-1. Module Overview

01:05

6-2. Representing Images as Matrices

03:03

6-3. Exploring the Fashion MNIST Dataset

06:07

6-4. Classifying Images Using Logistic Regression

04:05

6-5. Summary and Further Study

01:20

顶部