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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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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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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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#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics

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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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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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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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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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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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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#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist

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

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

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A thread on AUC Score (Area under the ROC Curve) Interpretation in #DataScience #MachineLearning

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"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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"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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AUC ranges in value from 0 to 1.

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AUC ranges in value from 0 to 1.

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