Google AI Princeton: Current and Future Research
Google has long partnered with academia to advance research, collaborating with universities all over the world on joint research projects which result in novel developments in Computer Science, Engineering, and related fields. Today we announce the latest of these academic partnerships in the form of a new lab, across the street from Princeton University’s historic Nassau Hall, opening early next year. By fostering closer collaborations with faculty and students at Princeton, the lab aims to broaden research in multiple facets of machine learning, focusing its initial research efforts on optimization methods for large-scale machine learning, control theory and reinforcement learning. Below we give a brief overview of the research progress thus far.
Observable – JavaScript Notebooks
Discover insights faster and communicate more effectively with interactive notebooks for data analysis, visualization, and exploration.
Deep automation in machine learning
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
AzureStor: an R package for working with Azure storage
A few weeks ago, I introduced the AzureR family of packages for working with Azure in R. Since then, I’ve also written articles on how to use AzureRMR to interact with Azure Resource Manager, how to use AzureVM to manage virtual machines, and how to use AzureContainers to deploy R functions with Azure Kubernetes Service. This article is the next in the series, and covers AzureStor: an interface to Azure storage.
Synthetic data generation – a must-have skill for new data scientists
Data is the new oil and truth be told only a few big players have the strongest hold on that currency. Googles and Facebooks of this world are so generous with their latest machine learning algorithms and packages (they give those away freely) because the entry barrier to the world of algorithms is pretty low right now. Open source has come a long way from being christened evil by the likes of Steve Ballmer to being an integral part of Microsoft. And plenty of open source initiatives are propelling the vehicles of data science, digital analytics, and machine learning.
What Kagglers are using for Text Classification
With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. For those who don’t know, Text classification is a common task in natural language processing, which transforms a sequence of text of indefinite length into a category of text. How could you use that?
Building your own Artificial Neural Network from scratch on Churn Modeling Dataset using Keras in Python
It is no secret that customer retention is a top priority for many companies;acquiring new customers can be several times more expensive than retaining existing ones. Furthermore, gaining an understanding of the reasons customers churn and estimating the risk associated with individual customers are both powerful components of designing a data-driven retention strategy. A churn model can be the tool that brings these elements together and provides insights and outputs that drive decision making across an organization.So it is used to predict as to which customers will be leaving the bank.
Like this:
Like Loading…
Related