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.
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.
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.
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.
