According to Ram Ramanathan of Google, there are three key scenarios in e-commerce that can be improved with machine learning: finding products of interest, recommending relevant items, and analyzing customer feedback. This may be applied to a variety of industries, including retail, telco, etc.

At Google Cloud Next 2017 in San Francisco, Ram overviewed how TensorFlow and Google Cloud Machine Learning can help to implement these scenarios. The session also featured a demo of a recommendation engine built for a TV services provider, suggesting relevant programs.

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At TensorBeat 2017, a panel discussed the use of machine learning for various tasks in manufacturing and the Industrial Internet of Things (IIoT). The speakers revealed which reasons were driving TensorFlow adoption within their organizations, exemplified real-life scenarios, and questioned the future of data.

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Sometimes, your models need to perform different computations depending on intermediate results or random chance. However, placing this sort of logic in the Python layer adds extra complexity and overhead to your code.

TensorFlow provides a number of native operations to help create graphs with built-in logical branching structure. At TensorBeat 2017, Sam Abrahams, a machine learning engineer at Metis, demonstrated how to make use of them.

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Digital assistants built with machine learning solutions are gaining their momentum. At TensorBeat 2017, one of the sessions covered how to deliver an answer bot with Keras and TensorFlow, what tools may help to address the issues, as well as tips on training a model and improving prediction results.

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At TensorBeat 2017, one of the sessions looked into the value brought by deep learning solutions to the financial sector. This blog post features some of the insights, focusing on employing TensorFlow for analyzing data in the foreign exchange market.

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Combined with GPUs, TensorFlow poses a powerful instrument for deep learning. However, it may still be difficult to switch between CPUs and GPUs—both in a local environment and in the cloud. One of the sessions at TensorBeat 2017 explored the ways to address the associated challenges and to maximize the usage of computational powers with Elastic GPUs.

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Deep learning solutions has penetrated multiple industries with a view to improve our everyday experiences. Deep learning is employed in translation, medicine, media and entertainment, cybersecurity, automobile industry, etc.

A discussion at a recent TensorFlow meetup explored the approaches and algorithms that drive deep reinforcement learning forward, what pitfalls may occur, and what are the perks of GPU-based training.

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Since its launch in 2015, TensorFlow has grown into a major player on machine learning scene. Being actively developed, the tool now offers a bunch of stable low-level APIs with some high-level ones being implemented at a rapid pace.

From this blog post, learn about APIs cultivated both inside the core TensorFlow project—including the ones for different languages—and outside it, still worth checking out.

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Machine learning techniques are widely employed to aid doctors in diagnosing. Still, enabling precise image recognition and analysis can be a real painful experience. This blog post explores why it happens and seeks the ways out as discussed at TensorBeat 2017.

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One of the sessions at TensorBeat 2017 explored the tool that supplements TensorFlow operations through gathering data related to the model’s performance (GPU/CPU usage, memory consumption, and disk I/O). This blog post looks into the capabilities of the solution, as well provides code samples to run commands.

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