2018 Deep Learning Testing 
KDnuggets
JUNE 11, 2025
Spotify Million Playlist Released for RecSys 2018, this dataset helps analyze short-term and sequential listening behavior. Resources like MovieLens and Netflix Prize remain foundational for benchmarking and testing ideas. Yelp Open Dataset Contains 8.6M reviews, but coverage is sparse and city-specific.
O'Reilly on Data
MAY 21, 2019
This is a good time to assess enterprise activities, as there are many indications a number of companies are already beginning to use machine learning. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.
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O'Reilly on Data
MARCH 7, 2019
Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). IBM Watson NLU.
Corinium
JUNE 6, 2019
Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. There is usually a steep learning curve in terms of “doing AI right”, which is invaluable. What is the most common mistake people make around data?
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.
Insight
MAY 16, 2019
It is a high-level, multifaceted field that allows machines to iteratively learn and understand complex representations from images and videos to automate human visual tasks. How Deep Learning scales based on the amount of Data [Copyright: Andrew Ng ]. Transfer Learning?—?YOLO. Precision?—?Recall
Domino Data Lab
JANUARY 20, 2019
What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? TCAV “uses directional derivatives to quantify the degree to which a user-defined concept is important to a classification result” ( Kim et al 2018 ). Introduction.
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