Remove 2018 Remove Deep Learning Remove Experimentation Remove Interactive
article thumbnail

6 trends framing the state of AI and ML

O'Reilly on Data

Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.

article thumbnail

Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

It is also important to have a strong test and learn culture to encourage rapid experimentation. The use of newer techniques, especially Machine Learning and Deep Learning, including RNNs and LSTMs, have high applicability in time series forecasting. Fractal’s 2018 Net Promoter Score is greater than 70.

Insurance 250
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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.

article thumbnail

Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

AutoPandas was created at UC Berkeley RISElab and the general idea is described in the NeurIPS 2018 paper “ Neural Inference of API Functions from Input–Output Examples ” by Rohan Bavishi, Caroline Lemieux, Neel Kant, Roy Fox, Koushik Sen, and Ion Stoica. Program Synthesis Papers at ICLR 2018 ” – Illia Polosukhin (2018-05-01).

Metadata 105
article thumbnail

Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

2018-06-21). For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.