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π΄ Computer Vision Tutorial 4οΈβ£: Edge detection

π‘ Jupyter Notebook π in second tweet.

Check this out π

#programming #MachineLearning #DataScience #pythonprogramming #CodeNewbie #pythoncode #100daysofcode #pythontricks #pythonprojects #100daysofcodechallenge #python #opencv

π‘ Jupyter Notebook π in second tweet.

Check this out π

#programming #MachineLearning #DataScience #pythonprogramming #CodeNewbie #pythoncode #100daysofcode #pythontricks #pythonprojects #100daysofcodechallenge #python #opencv

π΅ Find Jupyter Notebook π β¬οΈ

github.com/patchy631/twitβ¦

github.com/patchy631/twitβ¦

Hope you enjoyed reading!! π

Follow me if you are interested in:

β Python π

β Machine learning π€

β Computer Vision π₯

β MlOps βοΈ

Retweet π the first tweet π₯ .

Cheers!! π»

Follow me if you are interested in:

β Python π

β Machine learning π€

β Computer Vision π₯

β MlOps βοΈ

Retweet π the first tweet π₯ .

Cheers!! π»

π΄ Pandas πΌ Tutorial 3οΈβ£

π‘ Iterrows π€ π€ Itertuples

Check this out π

#programming #MachineLearning #DataScience #pythonprogramming #CodeNewbie #pythoncode #100daysofcode #pythontricks #pythonprojects #100daysofcodechallenge #python #Pandas

π‘ Iterrows π€ π€ Itertuples

Check this out π

#programming #MachineLearning #DataScience #pythonprogramming #CodeNewbie #pythoncode #100daysofcode #pythontricks #pythonprojects #100daysofcodechallenge #python #Pandas

π΅ If you wish to understand namedtuples π

Find Jupyter Notebook π β¬οΈ

github.com/patchy631/twitβ¦

github.com/patchy631/twitβ¦

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

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

Let's understand what these Transformers are all about

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

2/ #Transformers architecture follows Encoder and Decoder structure.

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

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

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

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

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

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

#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

1/n

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

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

1/n

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

2/n

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

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

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

1/ One way to test whether a time series is stationary is to perform an augmented Dickey-Fuller test - A Thread

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

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

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.

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

HA: The time series is stationary.

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

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

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

#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.

#DataScience #Statistics #DeepLearning #ComputerVision #100DaysOfMLCode #Python #programming #ArtificialIntelligence #Data

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

#DataScience #Statistics #DeepLearning #ComputerVision #100DaysOfMLCode #Python #programming #ArtificialIntelligence #Data

1/ When it is important to standardize variables in #DataScience #MachineLearning ? - A Thread

#DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics #programming #ArtificialIntelligence #Data #Stats #Database #BigData #100DaysOfCode

#DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics #programming #ArtificialIntelligence #Data #Stats #Database #BigData #100DaysOfCode

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.

#DataScience #MachineLearning #DeepLearning

#DataScience #MachineLearning #DeepLearning

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.

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

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

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

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

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.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

"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.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

3/16

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

Outlier Detection with Alibi Detect Library - A Thread

1/n

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #pythoncode #AI #ArtificialIntelligence

1/n

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #pythoncode #AI #ArtificialIntelligence

2/n

Alibi Detect is a Python library for detecting outliers, adversarial data, and drift. Accommodates tabular data, text, images, and time series that can be used both online and offline. Both TensorFlow and PyTorch backends are supported

#DataScience #DeepLearning

Alibi Detect is a Python library for detecting outliers, adversarial data, and drift. Accommodates tabular data, text, images, and time series that can be used both online and offline. Both TensorFlow and PyTorch backends are supported

#DataScience #DeepLearning

3/n

Supports a variety of outlier detection techniques, including Mahalanobis distance, Isolation forest, and Seq2seq

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

Supports a variety of outlier detection techniques, including Mahalanobis distance, Isolation forest, and Seq2seq

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

1/ Can you classify something without seeing it before - that's what Zero-Shot Learning is all about - A Thread

π One of the popular methods for zero-shot learning is Natural Language Inference (NLI).

#DataScience #DeepLearning #MachineLearning #100DaysOfMLCode #Pytho

π One of the popular methods for zero-shot learning is Natural Language Inference (NLI).

#DataScience #DeepLearning #MachineLearning #100DaysOfMLCode #Pytho

2/ Zero-shot learning is when we test a model on a task for which it hasn't been trained.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData

3/ In Zero-shot classification, we ask the model to classify a sentence to one of the classes (label) that the model hasn't seen during training.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI

1/ Why do we need the bias term in ML algorithms such as linear regression and neural networks ? - A thread

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode

2/ In linear regression, without the bias term your solution has to go through the origin. That is, when all of your features are zero, your predicted value would also have to be zero.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

3/ However that may not be the attributes of the training dataset.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData #DataAnalytics

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData #DataAnalytics

Google has released Imagen: a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation.

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode

This generator is scarily accurate with super-resolution! "A photo of a raccoon wearing an astronaut helmet, looking out of the window at night."

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

What is p-value - A thread

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

1/n

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

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

1/n

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

2/n

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

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

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

1/ #MachineLearning #Interview questsion -

Why L1 regularizations causes parameter sparsity whereas L2 regularization does not?

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats

Why L1 regularizations causes parameter sparsity whereas L2 regularization does not?

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats

2/ L1 & L2 regularization add constraints to the optimization problem. The curve H0 is the hypothesis. The solution to this system is the set of points where the H0 meets the constraints.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

3/ Regularizations in statistics or in the field of machine learning is used to include some extra information in order to solve a problem in a better way.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data

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

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

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.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

"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.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

3/16

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

How to Create Small Multiple Charts in Python, with Plotly

π§΅[1/23]

sharpsightlabs.com/blog/plotly-smβ¦

#python #datascience #pythoncode #datavisualization

π§΅[1/23]

sharpsightlabs.com/blog/plotly-smβ¦

#python #datascience #pythoncode #datavisualization

[2/]

Before I get into the mechanics of how to create a small multiple charts in Python, let me quickly explain why they are so important.

Before I get into the mechanics of how to create a small multiple charts in Python, let me quickly explain why they are so important.

[3/]

Small multiple charts are one of my favorite chart types.

They are very powerful, and also highly under-used.

#datascience #dataanalytics #datavisualization

Small multiple charts are one of my favorite chart types.

They are very powerful, and also highly under-used.

#datascience #dataanalytics #datavisualization

In Python ...

You can combine Numpy arrays vertically or horizontally using np.concatenate

#Python #pythoncode #datascience

You can combine Numpy arrays vertically or horizontally using np.concatenate

#Python #pythoncode #datascience

The first argument to the function is a list (or collection) of arrays that you want to combine.

You can actually combine many arrays ...just put them inside the list.

You can actually combine many arrays ...just put them inside the list.

The axis parameter controls the direction along which you combine the arrays.

For 2D arrays ...

'axis = 0' combines vertically

'axis = 1' combines horizontally

For 2D arrays ...

'axis = 0' combines vertically

'axis = 1' combines horizontally

In Matplotlib ...

You can get the RGBA representation of a color with the to_rgba() function.

#Python #pythoncode #datavisualization

You can get the RGBA representation of a color with the to_rgba() function.

#Python #pythoncode #datavisualization

You'll notice that the output of to_rgba is a tuple with four floats: (%red, %green, %blue, alpha)

#Python

#Python

In RGBA, the alpha channel represents the opacity of the color, where:

β 0.0 is fully transparent

β 1.0 is fully opaque

β 0.0 is fully transparent

β 1.0 is fully opaque

In Python, you can visualize images with the Plotly IMshow function.

π§΅[1/8]

sharpsightlabs.com/blog/plotly-imβ¦

#Python #pythonlearning #datascience #datavisualization

π§΅[1/8]

sharpsightlabs.com/blog/plotly-imβ¦

#Python #pythonlearning #datascience #datavisualization

[2/8]

You can use Plotly IMshow for a few uses.

You can use it to plot heatmaps ...

But you can also use it to plot images.

You can use Plotly IMshow for a few uses.

You can use it to plot heatmaps ...

But you can also use it to plot images.

[3/8]

The syntax for Plotly IMshow is pretty simple.

You call the function as px.imshow and then provide the name of the image file you want to visualize.

(This assumes you've imported Plotly express as px)

#Python #pythoncode

The syntax for Plotly IMshow is pretty simple.

You call the function as px.imshow and then provide the name of the image file you want to visualize.

(This assumes you've imported Plotly express as px)

#Python #pythoncode

In Python ...

You can use the Pandas dropna method to drop rows with missing values.

#Python #pythoncode #datascience

You can use the Pandas dropna method to drop rows with missing values.

#Python #pythoncode #datascience

As seen above, you can limit dropna to specific columns with the 'subset=' parameter.

With 'subset=', you can specify the columns in which dropna will look for missing values

#Python #pythonlearning #datascience

With 'subset=', you can specify the columns in which dropna will look for missing values

#Python #pythonlearning #datascience

If it finds missing values in any of those columns, it will drop the row.

But it will ignore missing values in other columns.

But it will ignore missing values in other columns.

In Python ...

You can use Numpy all to test conditions about the properties of a Numpy array.

#Python #pythoncode #datascience

You can use Numpy all to test conditions about the properties of a Numpy array.

#Python #pythoncode #datascience

βοΈ

So for instance, in the example above, I test if all of the values are greater than 2, by column.

#Python

So for instance, in the example above, I test if all of the values are greater than 2, by column.

#Python

To do this, you need to know how np.all works ...

But you also need to know how to use axes.

#Python #pythonlearning

But you also need to know how to use axes.

#Python #pythonlearning

#Learn π§ π#python: Sometimes when programming in python they're situations when you want to copy the contents of an existing list into another. Python has several ways of achieving that. In this thread you will learn different ways of achieving that with the help of examples.

1) Using the equal (=) sign operator:

Using = operator you can copy the contents of an existing list onto another/new list. But there's a problem with this method which I will explain on the next section.

Using = operator you can copy the contents of an existing list onto another/new list. But there's a problem with this method which I will explain on the next section.

The problem with the above method is that if you modify the new copied_fruits list the original list (fruits) is modified too, this is because the copied list (copied_fruits) is referencing/ pointing to same fruits list in memory.