The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms.
Dylan Bargteil introduces TensorFlow’s capabilities through its Python interface. Starting at the low level of the TensorFlow graph, you’ll build fundamental machine learning tools like linear regression and softmax classification before combining them into a basic neural network. You’ll leave with an appreciation of the low-level operations underlying all neural networks.
Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with the Howard Hughes Medical Institute (HHMI). Dylan studied physics and math at the University of Maryland and holds a PhD in physics from New York University.
Get the Platinum pass or the Training pass to add this course to your package.
Comments on this page are now closed.
For exhibition and sponsorship opportunities, email strataconf@oreilly.com
For information on trade opportunities with O'Reilly conferences, email partners@oreilly.com
View a complete list of Strata Data Conference contacts
©2018, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • confreg@oreilly.com
Comments
Hi. I am wondering if I can share this material with the rest of the organization?