Overview of TensorFlow

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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

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

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.

《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)

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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