The Google AI Teacher Camp is for university academic/research
members The event covered concepts and application of Tensorflow. They would be sharing
ways to learn about core machine learning concepts, develop and hone
participants’ Machine Learning (ML) skills via Tensorflow, and deep dive into
ML on mobiles. The Google
AI Teacher Camp was held on 18th Jan 2018. I took a photo in the entry of Google
office.
I met my colleague in SEEM
Dept. CityU – Dr. Zhang Qingpeng.
Mr. Laurence Moroney was
the trainer of this Google AI Teacher Camp.
There were three topic
tonight and they were “Mobile Machine Learning – AI in the palm of your hand”, “TensorFlow
for Javascript” and “TensorFlow: Autograph for Easier Code”.
Firstly, Mr. Laurence
Moroney showed ML located in the technology trigger with sharp growth trend.
Then he explained the different scope of AI, ML and DL. (Remark: the subtitle in each diagram was employed Google instant translator.)
The different between Traditional Programming and Machine Learning was mentioned. For Traditional Programming, inputs are Rules and Data, and then output is answers. For Machine Learning, inputs are Answers and Data, and then output is Rules. After that he also mentioned about Model that its input is Data and output is Predictions.
He use activity detection
as example to explain different sport.
After that Mr. Moroney
demonstrated Tensorflow using Python to find out the Predicted Model (equation)
by data X and Y.
He also used Fashion-MNIST (which
is a dataset of Zalando's article images—consisting of a training set of 60,000
examples and a test set of 10,000 examples.) to import into tensorflow for
training.
The following diagram
showed TensorFlow and TensorFlow Lite in Training Phase and Inference Phase,
respectively.
And then he briefed to
transfer the model from workstation to mobile using TensorFlow Lite Format.
A video “TensorFlow: an ML
platform for solving impactful and challenging problems” was demonstrated.
The second topic was
TensorFlow for JavaScript. Firstly, Mr.
Moroney demonstrated the ML structure included Data, Features, Hidden Layers
and Output. In-browser ML needed no
drivers/No install, but had interactive, sensors and data stays on the
client.
Then Mr. Moroney introduced TensorFlow.js and its API structure. (TensorFlow.js is an open source WebGL-accelerated JavaScript library for machine intelligence. It brings highly performant machine learning building blocks to your fingertips, allowing you to train neural networks in a browser or run pre-trained models in inference mode.) And then he showed the Core API to fit a polynomial codes and Layers API for Speech Command Recognition.
The coding and layer of NN was demonstrated and explained.
After that he used Pacman
game using webcam to control the movement of the Pacman.
Mr. Moroney also compared the speech in deferent hardware running TensorFlow.
The last session was TensorFlow Autograph. Mr. Moroney demonstrated Control flow in graphs.
TensorFlow Eager was
introduced at https://www.tensorflow.org/guide/eager
. Eager mode and Graph mode are
complementary.
AutoGraph was then
introduced that it helped to write complicated graph code using normal Python. Details at https://www.tensorflow.org/guide/autograph
. The AutoGraph features were described
as following diagram.
Finally, Mr. Moroney
mentioned the TensorFlow 2.0 would be coming. Moreover the training and deployment
structure was described. At the end,
Machine Learning Crash Course was introduced at https://developers.google.com/machine-learning/crash-course/
.
Reference:
A WebGL accelerated
JavaScript library for training and deploying ML models. https://js.tensorflow.org
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