TensorFlow for deep learning—implementing neural networks [electronic resource] / Buduma, Nikhil.

Format:

Book

Author/Creator:

Buduma, Nikhil, author.

Language:

English

Physical Description:

1 online resource (10 pages)

Edition:

1st edition

Place of Publication:

O'Reilly Media, Inc., 2016.

System Details:

text file

Summary:

Deep learning is a subset of machine learning, in the field of artificial intelligence. It's based on the idea that you can train a machine to learn from examples. A central method of training is through the use of neural networks. Why is it important? This lesson introduces you to TensorFlow, Google’s powerful open source software library for deep learning. What you’ll learn—and how you can apply it Learn how TensorFlow makes it easy for developers to design, build, and train deep learning models. This lesson shows you how to install TensorFlow and perform basic operations. Learn how to create and manipulate variables (taking advantage of CUDA if you have GPUs available on your computer). Compare TensorFlow with other frameworks for representing deep learning models. This lesson is for you because… You're a data scientist who is familiar with Python coding, and you need to learn how to implement neural networks using TensorFlow You're a Python developer who needs to work with deep learning models in production based on TensorFlow Prerequisites Familiarity with coding in Python Some familiarity with bash command line operations Basic understanding of machine learning Materials or downloads needed in advance Mac OS X or Linux computer Python and PIP

Notes:

Online resource; Title from title page (viewed August 15, 2016)

ISBN:

1-4919-6532-0