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May 28 • 6 tweets • 16 min read
Kullback-Leibler (KL) Divergence - A Thread

It is a measure of how one probability distribution diverges from another expected probability distribution.

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#DataScience #Statistics #DeepLearning #ComputerVision #100DaysOfMLCode #Python #programming #ArtificialIntelligence #Data #DataAnalytics #pythoncode #AI #MachineLearning #NeuralNetworks
#DataScience #Statistics #DeepLearning #ComputerVision #100DaysOfMLCode #Python #programming #ArtificialIntelligence #Data #DataAnalytics #pythoncode #AI #MachineLearning #NeuralNetworks
KL Divergence has its origins in information theory. The primary goal of information theory is to quantify how much information is in data. The most important metric in information theory is called Entropy

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Mathematical Expression of KL Divergence

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More from @rohanpaul_ai

Rohan Paul Profile picture
May 28
1/ "Software is eating the world. Machine learning is eating software. Transformers are eating machine learning."

Let's understand what these Transformers are all about

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2/ #Transformers architecture follows Encoder and Decoder structure.

The encoder receives input sequence and creates intermediate representation by applying embedding and attention mechanism.

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3/ Then, this intermediate representation or hidden state will pass through the decoder, and the decoder starts generating an output sequence.

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Read 14 tweets
Rohan Paul Profile picture
May 28
But what p-value means in #MachineLearning - A thread

It tells you how likely it is that your data could have occurred under the null hypothesis

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What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

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 A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

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Read 11 tweets
Rohan Paul Profile picture
May 28
1/ One way to test whether a time series is stationary is to perform an augmented Dickey-Fuller test - A Thread

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2/ H0: The time series is non-stationary. In other words, it has some time-dependent structure and does not have constant variance over time.

HA: The time series is stationary.

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3/ If the p-value from the test is less than some significance level (e.g. α = .05), then we can reject the null hypothesis and conclude that the time series is stationary.

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Read 8 tweets
Rohan Paul Profile picture
May 27
1/ When it is important to standardize variables in #DataScience #MachineLearning ? - A Thread

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2/ It is important to standardize variables before running Cluster Analysis. It is because cluster analysis techniques depend on the concept of measuring the distance between the different observations we're trying to cluster.

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3/ If a variable is measured at a higher scale than the other variables, then whatever measure we use will be overly influenced by that variable.

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Read 16 tweets
Rohan Paul Profile picture
May 27
Did you know how TensorFlow can run on a single mobile device as well as on an entire data center? Read this thread

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 Google has designed TensorFlow such that it is capable of dividing a large model graph whenever needed.

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3/n
 It assigns special SEND and RECV nodes whenever a graph is divided between multiple devices (CPUs or GPUs).

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Read 9 tweets
Rohan Paul Profile picture
May 27
A thread on AUC Score (Area under the ROC Curve) Interpretation in #DataScience #MachineLearning

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2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

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3/16

AUC ranges in value from 0 to 1.

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Read 16 tweets

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