5th March 2018 – Data Science News Daily Roundup

Tensorflow 1.6.0, Pandas on Ray, Google-Landmarks, Microsoft’s Custom Vision and  Face API and more in today’s top stories around machine learning, deep learning, and data science news.

1. Tensorflow v1.6.0 finally makes its debut

Tensorflow 1.6.0 has finally released after two release candidates. The breaking changes, major features, and improvements include:

• Pre-built binaries are now built against CUDA 9.0 and cuDNN 7.
• Pre-built binaries will use AVX instructions.
• New Optimizer internal API for non-slot variables.
• tf.estimator.{FinalExporter,LatestExporter} now export stripped SavedModels. FFT support added to XLA CPU/GPU.
• Android TF can now be built with CUDA acceleration on compatible Tegra devices.

To know about Bug Fixes and other changes, you may visit the GitHub repo.

2. Pandas on Ray, A DataFrame library for making Pandas faster

The team at UC Berkeley are developing a DataFrame library that wraps Pandas and transparently distributes the data and computation. The early stage library, Pandas on Ray, can accelerate Pandas queries by 4x on an 8-core machine, only requiring users to change a single line of code in their notebooks. Pandas on Ray is targeted towards existing Pandas users who are looking to improve performance and see faster runtimes without having to switch to another API. The ultimate goal of this project is to be able to use Pandas in a cloud setting.

3. Google launches Landmarks, new Dataset for Landmark Recognition

Google has released Google-Landmarks, the largest worldwide dataset for recognition of human-made and natural landmarks. The dataset contains more than 2 million images depicting 30 thousand unique landmarks from across the world and a number of classes that is ~30x larger than what is available in commonly used datasets. Additionally, Google is also open-sourcing Deep Local Features (DELF), an attentive local feature descriptor. They have also launched two Kaggle challenges. The recognition track challenge is to build models that recognize the correct landmark in dataset of challenging test images, while the retrieval track challenges participants to retrieve images containing the same landmark.

4. Microsoft Azure adds computer vision and image processing capabilities

Microsoft has updated its Azure platform with computer vision capabilities with the launch of Custom Vision, service that lets developers train models for processing specific kind of images. Alongside Custom Vision, the company also made its Face API service for face and emotion detection generally available. The major improvement in Face API includes a scalability boost that enables the service to recognize up to a million different individuals within images. It also launched Bing Entity Search, which allows developers to harness Microsoft’s search engine to help users find needed information within their application.

5. Intela launches Farrago, an online tool that uses machine learning to clean up dirty data

Data science company Intela AI, launches Farrago, a machine learning tool to clean up dirty data. This tool can automate the manual work of identifying and removing duplicate records from databases. It can also analyze a company’s data and intelligently recommend the best way to organize, clean and transform it. According to Intela CEO Asa Cox, “Farrago could save a company, client or programme, hundreds of man-hours of time spent manually (or semi-manually) cleaning data.” The online demonstration of Farrago is readily available.