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A thread - All the basic #Matrix #Algebra you will need in #MachineLearning #DeepLearning

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

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

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A matrix A is a rectangular array of scalars usually presented in the following form

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math #Data #AI #TensorFlow #programming #DataAnalytics #ArtificialIntelligence #Mathematics

A matrix A is a rectangular array of scalars usually presented in the following form

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Math #Data #AI #TensorFlow #programming #DataAnalytics #ArtificialIntelligence #Mathematics

A thread on AUC Score Interpretation

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

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

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roc_auc_score is defined as the area under the ROC 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 #DataScientist #Statistics #Math

roc_auc_score is defined as the area under the ROC 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 #DataScientist #Statistics #Math

Image interpolation occurs when you resize or distort your image from one pixel grid to another.

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#computervision #IMAGE #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #programming #Math #Stat #dataviz #DataAnalytics #AI #ArtificialIntelligence #data

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#computervision #IMAGE #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #programming #Math #Stat #dataviz #DataAnalytics #AI #ArtificialIntelligence #data

Image interpolation works in two directions, and tries to achieve a best approximation of a pixel's intensity based on the values at surrounding pixels.

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#computervision #IMAGE #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #programming #Math #Stat

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#computervision #IMAGE #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #programming #Math #Stat

Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur when you are correcting for lens distortion or rotating an image.

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#computervision #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

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#computervision #DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

Thanks @braveproductguy for sharing this set of Cognitive biases in #software #engineering

A. 𝐀𝐧𝐜𝐡𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐛𝐢𝐚𝐬 - work fills up the space you allot to it. Use budgets instead of estimates to build a quality feature

#webdevelopment

A. 𝐀𝐧𝐜𝐡𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐛𝐢𝐚𝐬 - work fills up the space you allot to it. Use budgets instead of estimates to build a quality feature

#webdevelopment

@braveproductguy B. 𝐀𝐧𝐜𝐡𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐛𝐢𝐚𝐬 - Missing links, lack of readability and documents leads people to use their own understanding of the system thereby making insufficient modifications.

@braveproductguy C. 𝐀𝐧𝐜𝐡𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐛𝐢𝐚𝐬 - Initial solution design or architecture design does not evolve sufficiently to accommodate new changes, requests or constraints. Developers who anchor on existing solutions tend to include unnecessary features.