TensorFlow Hub: The platform to share and discover pretrained models for TensorFlow
February 23, 2020
Machine learning is a difficult skill to master for the many developers who are starting to use TensorFlow. Many developers use TensorFlow today, yet the majority of software developers out there have yet to learn machine learning. Mike Liang takes you through TensorFlow Hub, designed to help developers make better and faster user of machine learning in their products.
Predicting the quality of life from satellite imagery
February 18, 2020
In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data.
Can behavioral analytics for enterprise security benefit from approaches in NLP?
February 7, 2020
While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice.
Dealing with data scarcity in natural language processing
January 13, 2020
In this age of big data, NLP professionals are all too often faced with a lack of data: written language is abundant, but labeled text is much harder to come by. Yves Peirsman outlines the most effective ways of addressing this challenge, from the semiautomatic construction of labeled training data to transfer learning approaches that reduce the need for labeled training examples.
The unreasonable effectiveness of transfer learning on NLP
January 5, 2020
Transfer learning has been proven to be a tremendous success in computer visiona result of the ImageNet competition. In the past few months, there have been several breakthroughs in natural language processing with transfer learning, namely ELMo, OpenAI Transformer, and ULMFit. David Low demonstrates how to use transfer learning on an NLP application with SOTA accuracy.
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills
January 2, 2020
Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.
Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark
December 27, 2019
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zooa unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipelineusing real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA.
Building high-performance text classifiers on a limited labeling budget
December 26, 2019
Robert Horton, Mario Inchiosa, and Ali Zaidi demonstrate how to use three cutting-edge machine learning techniquestransfer learning from pretrained language models, active learning to make more effective use of a limited labeling budget, and hyperparameter tuning to maximize model performanceto up your modeling game.