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[Stone River eLearning] Math for Machine Learning - [FCO] TGx Exclusive

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521.30 MB

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049f9792733ca74149fbd439dedbefcd218abbad

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11-01-2019 14:31

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File	Size
01.Course Promo.mp4	4.67 MB
02.Course Introduction.mp4	5.18 MB
03.Linear Regression.mp4	10.97 MB
04.The Least Squares Method.mp4	17.54 MB
05.Linear Algebra Solution to Least Squares Problem.mp4	17.91 MB
06.Example Linear Regression.mp4	6.00 MB
07.Summary Linear Regression.mp4	1.61 MB
08.Classification.mp4	1.67 MB
09.Linear Discriminant Analysis.mp4	967.07 kB
10.The Posterior Probability Functions.mp4	5.29 MB
11.Modelling the Posterior Probability Functions.mp4	11.32 MB
12.Linear Discriminant Functions.mp4	8.38 MB
13.Estimating the Linear Discriminant Functions.mp4	8.59 MB
14.Classifying Data Points Using Linear Discriminant Functions.mp4	4.95 MB
15.LDA Example 1.mp4	20.16 MB
16.LDA Example 2.mp4	26.94 MB
17.Summary Linear Discriminant Analysis.mp4	4.83 MB
18.Logistic Regression.mp4	1.63 MB
19.Logistic Regression Model of the Posterior Probability Function.mp4	4.22 MB
20.Estimating the Posterior Probability Function.mp4	12.92 MB
21.The Multivariate Newton-Raphson Method.mp4	16.62 MB
22.Maximizing the Log-Likelihood Function.mp4	21.45 MB
23.Logistic Regression Example.mp4	14.30 MB
24.Summary Logistic Regression.mp4	3.75 MB
25.Artificial Neural Networks.mp4	778.55 kB
26.Neural Network Model of the Output Functions.mp4	18.79 MB
27.Forward Propagation.mp4	1.61 MB
28.Choosing Activation Functions.mp4	5.91 MB
29.Estimating the Output Functions.mp4	3.02 MB
30.Error Function for Regression.mp4	3.34 MB
31.Error Function for Binary Classification.mp4	8.14 MB
32.Error Function for Multiclass Classification.mp4	6.26 MB
33.Minimizing the Error Function Using Gradient Descent.mp4	9.21 MB
34.Backpropagation Equations.mp4	6.15 MB
35.Summary of Backpropagation.mp4	2.31 MB
36.Summary Artificial Neural Networks.mp4	5.02 MB
37.Maximal Margin Classifier.mp4	3.11 MB
38.Definitions of Separating Hyperplane and Margin.mp4	8.45 MB
39.Proof 1.mp4	10.84 MB
40.Maximizing the Margin.mp4	5.34 MB
41.Definition of Maximal Margin Classifier.mp4	1.54 MB
42.Reformulating the Optimization Problem.mp4	12.22 MB
43.Proof 2.mp4	1.81 MB
44.Proof 3.mp4	7.29 MB
45.Proof 4.mp4	12.97 MB
46.Proof 5.mp4	8.14 MB
47.Solving the Convex Optimization Problem.mp4	1.65 MB
48.KKT Conditions.mp4	2.72 MB
49.Primal and Dual Problems.mp4	2.07 MB
50.Solving the Dual Problem.mp4	4.79 MB
51.The Coefficients for the Maximal Margin Hyperplane.mp4	677.02 kB
52.The Support Vectors.mp4	1.34 MB
53.Classifying Test Points.mp4	2.49 MB
54.Maximal Margin Classifier Example 1.mp4	14.39 MB
55.Maximal Margin Classifier Example 2.mp4	16.72 MB
56.Summary Maximal Margin Classifier.mp4	1.58 MB
57.Support Vector Classifier.mp4	5.35 MB
58.Slack Variables Points on Correct Side of Hyperplane.mp4	5.47 MB
59.Slack Variables Points on Wrong Side of Hyperplane.mp4	2.19 MB
60.Formulating the Optimization Problem.mp4	5.46 MB
61.Definition of Support Vector Classifier.mp4	1.20 MB
62.A Convex Optimization Problem.mp4	3.30 MB
63.Solving the Convex Optimization Problem (Soft Margin).mp4	9.41 MB
64.The Coefficients for the Soft Margin Hyperplane.mp4	2.92 MB
65.Classifying Test Points (Soft Margin).mp4	2.38 MB
66.The Support Vectors (Soft Margin).mp4	2.32 MB
67.Support Vector Classifier Example 1.mp4	22.06 MB
68.Support Vector Classifier Example 2.mp4	14.34 MB
69.Summary Support Vector Classifier.mp4	1.94 MB
70.Support Vector Machine Classifier.mp4	1.66 MB
71.Enlarging the Feature Space.mp4	8.21 MB
72.The Kernel Trick.mp4	6.86 MB
73.Summary Support Vector Machine Classifier.mp4	3.40 MB
Discuss.FreeTutorials.Us.html	165.68 kB
FreeCoursesOnline.Me.html	108.30 kB
FreeTutorials.Eu.html	102.23 kB
How you can help Team-FTU.txt	259.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B
[TGx]Downloaded from torrentgalaxy.org.txt	524.00 B

Description

Author: Richard Han
Publisher[b/]: Stone River eLearning
[b]Release Date: May 2018
ISBN: 200000006A0200
Language: English
Torrent Contains: 79 Files
Course Source: https://www.oreilly.com/library/view/math-for-machine/200000006A0200/

Video Description

Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science Data Science Artificial Intelligence If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable—courses in linear algebra, discrete math, and calculus.

Table of Contents

• Course Promo
• Introduction
• Linear Regression
• Linear Discriminant Analysis
• Logistic Regression
• Artificial Neural Networks
• Maximal Margin Classifier
• Support Vector Classifier
• Support Vector Machine Classifier.

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