Overview of TensorFlow

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#tensorflow

本文介绍了TensorFlow这一开源软件库，它使用数据流图进行数值计算，最初由Google Brain团队开发，用于机器学习和深度神经网络研究。TensorFlow具备广泛的适用性，并提供了丰富的功能和工具集，支持从桌面设备到服务器集群等多种平台上的部署。此外，文章还列举了使用TensorFlow的一些知名企业和项目案例。
What’s Tensorflow?
  • Open source software library for numerical computation using data flow graphs.
  • Originally developed by Google Brain Team to conduct machine learning and deep neural networks research.
  • General enough to be applicable in a wide variety of other domains as well.

TensorFlow provides an extensive suite of functions and classes that allow users to build various models from scratch

Why TensorFlow?
  • Python API
  • Portability: deploy computation to one or more CPUs or GPUs in a desktop,server, or mobile device with a single API
  • Flexibility: from Raspberry Pi, Android, Windows, iOS, Linux to server farms
  • Visualization (TensorBoard is da bomb)
  • Checkpoints (for managing experiments)
    Auto-differentiation autodiff (no more taking derivatives by hand. Yay)
  • Large community (> 10,000 commits and > 3000 TF-related repos in 1 year)
  • Awesome projects already using TensorFlow
Companies using Tensorflow
  • Google
  • OpenAI
  • DeepMind
  • Snapchat
  • Uber
  • Airbus
  • eBay
  • Dropbox
    A bunch of startups
Some cool projects using TensorFlow
  • Neural Style Translation
  • Generative Handwriting
  • WaveNet: Text to Speech
Goals
  • Understand TF’s computation graph approach
  • Explore TF’s built-in functions
  • Learn how to build and structure models best suited for a deep learning project.
Books
  • 《TensorFlow for Machine Intelligence》 (TFFMI)
  • 《Hands-On Machine Learning with Scikit-Learn and TensorFlow》 Chapter 9: Up and running with TensorFlow
  • 《Fundamentals of Deep Learning》 Chapter 3:Implementing Neural Networks in TensorFlow (FODL)
Website
Simplified TensorFlow?
  • TF Learn (tf.contrib.learn): simplified interface that helps users transition from the the world of one-liner such as scikit-learn
  • TF Slim (tf.contrib.slim): lightweight library for defining, training and evaluating complex models in TensorFlow.
  • High level API: Keras, TFLearn, Pretty Tensor

But we don’t need baby Tensorflow …

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