May 25 10 tweets 20 min read
What is p-value - 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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P values address only one question:
How likely are your data, assuming a true null hypothesis ?

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p-value does this by calculating the likelihood of your test statistic. The p-value gets smaller as the test statistic gets further away from the range of test statistics predicted by the null hypothesis.

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The p-value is a proportion: if your p-value is 0.05, that means that 5% of the time you would see a test statistic at least as extreme as the one you found if the null hypothesis was true.

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What is significance level

The significance level, or α, is the probability of rejecting the null hypothesis when it is true. e.g. a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference
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Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant.

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If P value is more than significance level we accept the null hypothesis or fail to reject it,

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So, P-values are often interpreted as your risk of rejecting the null hypothesis of your test when the null hypothesis is actually true.

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# More from @rohanpaul_ai

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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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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#DataScience #DeepLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat
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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.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning
3/n
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

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math
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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May 28
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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May 27
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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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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It assigns special SEND and RECV nodes whenever a graph is divided between multiple devices (CPUs or GPUs).

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