Tensorflow Meetup

  • Date: June 7 2017
  • Venue:

Tensorflow - Magnus Hyttsten and Marion Le Borgne

Google Developer Group Silicon Valley 320 Google Developers

Open source machine learning Magnus Hyttsten @magnushyttsten

What’s up today Thoughts on deep learning Open Source Models Image classification Natural language processing Generate your own artwork Resources you can use

https://www.youtube.com/watch?v=9ziVGkt8Gg4 Most of the slides are the same as the above video on google.io, Josh Gordon

No slide available online later.

I’m taking pictures. Slide 1

Slide 2

Slide 3: Cat or Dog

Slide 4: Tensorflow

Slide 5: Graph

Slide 6:

Slide 7:

Developing with Deep Learning

Slide 8:

Slide 9: Keras -> sits on top of TF TF-learn Code example

Slide 10: Slide 11:

goo.gl/TjQPfS https://github.com/random-forests/tensorflow-workshop/tree/master/extras

12: tensorboard, embedding

13: 3d tensorboard, clustering mnist

14: playground.tensorflow.org

15: TPU (tensor processing unit)

Search Jeff Dean 16: computer picture -> server room

17: server room 2

18: then switch to blog: mul and add cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-google-first-tensor-proessing-unit-tpu

19: inception 20: cloud.google.com/vision 21: using picture from new york 22: imagenet Dog breed and pictures 23: dog checking 24: graphi again Everything sums to 1

Transfer learning Throw the last layer out and use your 10 to 100 images and get it wo Replace output layer with nodes for your classes. Most of the network is unchanged Only last layer of weights are updated

25: application Tensorflow for poets.

26: design your experiment 27: 3 phones apps Blend artistic styles Classification Object detection 28: show and tell: description 29: deep dream

  1. Style transfer Take the style + golden gate Paint golden gate bridge into picaso style
  2. How does it work

Style, Content -> edges, textures, patterns, object, predictons, style loss, content loss, total loss

Target -> You can do this realtime Real dog camera video + sytle -> different

  1. Language SuntaxNet: Parsey McParseface, Parsey Saurus

I love NYC https://cloud/google.com/natural-language

Start a container - instructions at goo.gl/aH5wLP

“The gostak distims the doshes” Andrew Ingraham ->

From the internacctive text analyzer, insdie the syntaxnet container

Answer is verb Parse_tree, _ = annotate_text(‘the gos…’) Token = parse_tree.token For t in tokens: If t.word == ‘distims’: Print ‘Answer is ‘, pos(t.tag)

Answer is: Verb

  1. What ifThe sentens is in german


    All the languages are supported

More shared research in #tensorflow Neural audio synthesis,

Learning more Cs20si,stanford Cs231n.stanford

goo.gl/KewA03 -> machine learning recipes https://github.com/machine-learning-projects/machine-learning-recipes https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal

Chris olah’s blog Colah.github.io *****

Many tutorials: tensorflow.org

Temporal Anomaly Detection in Streaming Data with LSTM networks

Marion Le Borgne [email protected]

Explosion of sensor data

Preventative Maintenance Iot Sensors: Adjust energy usage in connected building Traffic Patterns: Identify unusual patterns from – Types of Anomalies Spatial Anomalies (out of bounds) Temporal Anomalies (sequences matter) – Scalable Anomaly Detection Ability to scale individual detectors to lots of data streams Ability to detect anomalies without prior knowledge Train – Anomaly Detection Benchmark numenta.com/numenta-anomaly-benchmark – Anonmaly Detection with LSTMs

Link to the slides will be available online.

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