TensorFlow is Google’s open source framework for machine intelligence. Eli Bixby gives a conceptual overview of TensorFlow and provides a short example of putting the concepts to use with TFLearn.
Eli begins with a very brief introduction to deep learning, discussing what separates deep learning from other machine-learning algorithms and why it’s suddenly so relevant, before exploring neural nets, a common example of deep learning. Eli then turns to TensorFlow, explaining how it relates to deep learning and offering an overview of core TensorFlow concepts: operations, tensors, graphs sessions, placeholders and devices, how graphs are defined in TensorFlow, how they are executed, and what can be done with them. Eli concludes by walking you through how to define and execute a simple model using TFLearn, TensorFlow’s high-level API wrapper.
Eli Bixby is a developer programs engineer at Google currently developing on Google Cloud Platform’s DevOps distributed systems, machine-learning, and big data offerings. He joined Google as a developer programs engineer. Previously, Eli dabbled in several research areas, with papers in biophysics, algorithmic game theory, and most recently computational biology.
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