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Advanced NLP With Scipy
Deep Learning By Ian Goodfellow
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I Will Teach You To Be Rich
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How To Choose Kernel In SVM
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How XGBoost Handle Missing Values
How-To-Read-Paper
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Kernel In SVM
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Quotient Rule Or Differentiation Of Division
Qustions To Ask In A Interview?
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Root Mean Squared Logarithmic Error (Rmsle)
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ROUGE-LSUM Score
ROUGE-N Score
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Scalar
Second Order Derivative Or Hessian Matrix
Semi-supervised Learning
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Sigmoid Function
Singular Value Decomposition (Svd)
Soft Margin In SVM
Softmax
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Softsign
Some Common Behavioral Questions
Sources Of Uncertainty
Spacy-Doc-Object
Spacy-Doc-Span-Token
Spacy-Explanation-Of-Labels
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Splitting Tree In Decision Tree
Stacking Or Meta Model In Ensemble Learning
Standard Deviation
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Standardization Or Normalization
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Stride In CNN
Stump
Supervised Learning
Support Vector
Support Vector Machine (Svm)
Surprise
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Swallow Vs. Deep Learning
Tanh
Text Preprocessing
TF-IDF
Three Way Partioning
Trace-Operator
Training A Deep Neural Network
Transformer Timeline
Triplet Loss
True Positive Rate
Undercomplete Autoencoder
Undersampling
Unit-Vector
Unsupervised Learning
Untitled
Vanishing Gradient
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Vector
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Loss Vs. Cost
[!def] Loss vs. Cost
Cost is the average of all the loss values for current batch/dataset
AKA empirical risk