Google open-sources TensorFlow training tools

Over the past year, Google’s TensorFlow has asserted itself as a popular open source toolkit for deep learning. But training a TensorFlow model can be cumbersome and slow—especially when the mission is to take a dataset used by someone else and try to refine the training process it uses. The sheer number of moving parts and variations in any model-training process is enough to make even deep-learning experts take a deep breath.

This week, Google open-sourced a project intended to cut down on the amount of work in configuring a deep learning model for training. Tensor2Tensor, or T2T for short, is a Python-powered workflow organization library for TensorFlow training jobs. It lets developers specify the key elements used in a TensorFlow model and define the relationships among them.

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Why the falling oil price isn’t hurting markets

INVESTORS could easily get confused about the impact of oil-price rises on the economy and markets. The story seemed to be clear: high prices bad, low prices good. The two great oil shocks in the 1970s were unambiguously bad for Western economies—ushering in stagflation and transferring spending power to the oil-producing countries. In turn, low oil prices in the late 1990s coincided with the dotcom boom.

But when oil fell in the second half of 2015, that was seen as a bearish sign for the global economy and markets. Now oil is falling again, with both Brent crude and West Texas intermediate dropping more than 20%. But the decline has barely made a dent in the upward march of the S&P 500 index.

The key to the differing market reaction is why the oil price is falling. Back in 2015, the fear was falling demand. Investors worried in particular that the Chinese economy was slowing. If that assumption had been right, demand for much more than oil would have suffered. The equity markets did not…Continue reading

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