Domino Data Lab

AUGUST 22, 2019

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.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Sisense

MAY 26, 2020

While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. Using the semantic meaning of words it already knows as a base, the model can understand the meanings of words it doesn’t know that appear in test data.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

O'Reilly on Data

DECEMBER 12, 2019

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] 2] The Security of Machine Learning. [3]

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

Domino Data Lab

DECEMBER 1, 2019

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.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. The data has been collected as part of a research collaboration between Worldline and the Machine Learning Group of Université Libre de Bruxelles. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

JULY 9, 2019

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.

Data Science 112

Data Science Machine Learning Advertising Modeling 112