big data

In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, we’re introduced to random features for large-scale kernel machines, and we take a listener question about the size of computing power in machine learning. 

The Economic Impact of Machine Learning and Using The Kernel Trick To Dig In To Big Data

In episode fifteen we talk with Max Welling, of the University of Amsterdam and University of California Irvine. We talk with him about his work with extremely large data and big business and machine learning. We take a listener question about the relationship between machine learning and artificial intelligence. Plus, we get an introduction to change point detection. 

Really Really Big Data and Machine Learning in Business

“Artificial intelligence (AI) can be seen as a progression in our scalability of labor.” This quote comes from this week’s guest, Naveen Rao, who received his PhD in Neuroscience from Brown before becoming CEO at Nervanasys, which works on full stack solutions to help companies solve machine learning (ML) problems at scale. In this week’s episode, Rao speaks about certain domains in industry where he feels optimistic about machine learning (ML) making a difference in the next five to 10 years, providing interesting perspectives that include advances in the areas of agriculture and oil &amp; gas.

Follow the Data: Deep Learning Leads the Transformation of Enterprise

Artist: Squim
Album: Synaptic Seepage
TINGOTONGOTAPES
2017
c60 white cassette tape

06 - Big Data Deep Dump

In this episode of the Data Show, I spoke with Lukas Biewald, co-founder and chief data scientist at CrowdFlower. In a previous episode we covered how the rise of deep learning is fueling the need for large labeled data sets and high-performance computing systems. CrowdFlower has a service that many leading companies have come to rely on to provide them with labeled data sets to train machine learning models. As deep learning models get larger and more complex, they require training data sets that are bigger than those required by other machine learning techniques.

Data Preparation in the Age of Deep Learning

In this episode of the Data Show, I spoke with Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart. The objective is to get fresh groceries from popular retailers delivered to customers in a timely fashion. Instacart’s goals land them in the center of the many opportunities and challenges involved in building high-impact data products.

Data Science and Deep Learning in Retail

Alex thinks dropout is cool. He's still not quite sure what batch normalization is. Regarding ImageNet competition, Apurva, along with offering tips to staying motivated to learning  says that instead of creating "new" models, people are only doing ensembling now to get a marginal edge over everyone else. Edderic announces revamping his PC workstation for deep learning (bye-bye Amazon!)

big data

Radhouane Janfaoui