Remove 2013 Remove Deep Learning Remove Statistics Remove Testing
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Because ML models can react in very surprising ways to data they’ve never seen before, it’s safest to test all of your ML models with sensitivity analysis. [9]

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Data Drift Detection for Image Classifiers

Domino Data Lab

Given the proliferation of interest in deep learning in the enterprise, models that ingest non traditional forms of data such as unstructured text and images into production are on the rise. Step 4: Generate the test, train and noisy MNIST data sets. Detecting image drift. x_test = x_test.astype('float32') / 255.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. describe().

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Data Science at The New York Times

Domino Data Lab

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion. .”