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1.

tutorial

Support Vector Machine (SVM) Theory

In this tutorial, we'll describe the mathematics behind Support Vector Machines. This tutorial is going to be much more math heavy as compared to other tutorials. If you're mostly interested in applying SVMs to solve problems, then our first tutorial on SVMs is sufficient. However, if you would like to understand the mathematical basis of Support Vector Machines, then you'll find this tutorial interesting.

In this tutorial, we will focus on the hard-margin SVM and soft-margin SVM. However, we will not be considering kernels or the hyper-parameter \gamma (gamma).

In SVM, the decision function for predicting data points *x* corresponds to the equation of an hyperplane:

f_w(x) = w^T x + w_0

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Category: Machine Learning

NaN.

course

Learn Python for Data Science and Machine Learning

This 6-part course teaches python for data science and machine learning.

In this course, you will learn about the following important Python libraries used in Data Science and Machine Learning. Numpy (numerical python) provides vector and matrix primitives in Pytho...

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Syllabus

Start this course

Introduction to NumPy (follow along)

Category: Machine Learning

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NaN.

tutorial

Statistics: Central Tendency metrics, Dispersion and Correlation (Quick Review)

Statistics is a very broad branch of mathematics that deals with everything related to data, from collection and organization of data to its analysis, interpretation and presentation. With the ever increasing amount of data, statistics has become an indispensable tool in every field where one has to work with data.

When the amount of data we are dealing with is fairly small, then it might be possible to talk about all the data items individually. However, when we are dealing with large quantities of data, which is almost always the case in real world situations, we need to have some characteristic values that can represent the data.

In this tutorial, we'll introduce such measures first for a single variable. For example, say the weight of students in a particular school. These measures will include measures of central tendency and measures of dispersion. Then, we'll look at measures for und...

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Category: Machine Learning

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could u pl. explain whether we can extrapolate correlation to double implication for

hindu logicians / naya system ?

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I have already treated this particular topic already in college as a business student but i learnt it to pass my exams. And now i need to just go over again and i believe i will get it again; permanently this time lol.

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4.

discussion

Pro Content

Solution to Hands-on Project: Digit classification with K-Nearest Neighbors and Data Augmentation

Category: Machine Learning

6.

tutorial

Dropout (neural network regularization)

**Dropout **is a widely used regularization technique for neural networks. Neural networks, especially deep neural networks, are flexible machine learning algorithms and hence prone to overfitting. In this tutorial, we'll explain what is dropout and how it works, including a sample TensorFlow implementation.

If you [have] a deep neural net and it's not overfitting, you should probably be using a bigger one and using dropout, ... - Geoffrey Hinton [2]

Dropout is a regularization technique where during each iteration of gradient descent, we *drop* a set of neurons selected at random. By *drop*, what we mean is that we essentially act as if they do not exist.

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Category: Machine Learning

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