Insight

JANUARY 14, 2020

Will a network trained with fake data be able to generalize to the real world? Lauren Holzbauer was an Insight Fellow in Summer 2018. In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

JULY 22, 2019

We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. It’s also a driver for data science.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

datapine

NOVEMBER 19, 2019

An important part of artificial intelligence comprises machine learning, and more specifically deep learning – that trend promises more powerful and fast machine learning. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. One-fifth use reinforcement learning.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. 2018-06-21). training data”) show the tangible outcomes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105