Deep Learning by Doing arconsis IT-Solutions GmbH Who? Wolfgang - PowerPoint PPT Presentation

Deep Learning by Doing arconsis IT-Solutions GmbH

Who? Wolfgang Frank Achim Baier @wolfgangfrank @arconsis

Let’s talk about…

Cucumber

… we will get back to that later!

Why learn about Machine Learning?

Web Search News Search

…maybe more important… Cool & Fun

Inspirational Applications of Deep Learning Source: http://machinelearningmastery.com

Video posted on YouTube by Yann LeCun Yann LeCun's convolutional neural network 1993 10%-20% of all checks in US

Instant Visual Translation Example of instant visual translation, taken from the Google Blog. Automatic Machine Translation

Thomas Samson/AFP/Getty Images Google Translate 103 languages!

Colorization of Black and White Photographs Image taken from Richard Zhang, Phillip Isola and Alexei A. Efros. Automatic Colorization of Black and White Images Very large convolutional neural networks and supervised layers recreate the image with the addition of color.

Example of Object Detection within Photogaphs Taken from the Google Blog. Object Classification and Detection 
 in Photographs

Automatic Image Caption Generation Sample taken from Andrej Karpathy, Li Fei-Fei Automatic Image Caption Generation

Automatic Game Playing Deep reinforcement models learns how to play breakout based only on the pixels on the screen

Real-Time object detection Source: http://pjreddie.com/darknet/yolo/

Self driving vehicles Source: https://electrek.co/2016/12/21/tesla-autopilot-vision-neural-net-data-elon-musk/

Many more examples / applications … Source: deeplearning4j.org

source: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ Artificial Intelligence — Human Intelligence Exhibited by Machines Machine Learning — An Approach to Achieve Artificial Intelligence Deep Learning — A Technique for Implementing Machine Learning

“Machine learning is about using examples to develop an expert system that can make useful statements about new inputs.” Machine Learning with TensorFlow MEAP, Manning

Typical principles & algorithms…

Supervised Learning Train a model with known / labeled data to make predictions for 
 new data (regression, classification) regression: continuous value output classification>; discrete value output

Unsupervised Learning Find common structures in unknown / unlabeled data 
 (clustering, patterns, find coherent groups)

Reinforcement Learning Getting an agent to act in the world so as to maximize its rewards 
 (Trial & Error —> construct knowledge —> Map situations to actions)

Famous Algorithms Machine Learning Classic Algorithms Neural Networks Linear Regression Autoencoder Logistic Regression Q Policy NN Softmax Regression Perceptron K-means Convolutional NN Self-organizing map Recurrent NN Viberti Deep NN

A top down approach…

n*log(n) Arrays.sort(int[] a) —> tuned quicksort Arrays.sort(Object[] a) —> modified merge sort

Derive the “ back propagation” equation from first principles and implement it from scratch?

Instead, just call model.fit() on the deep learning API.

Top engineering and academic capabilities do not have to come first, they can come later.

Deep Learning for Industry 4.0 / IOT Japanese cucumber farmer is using deep learning and TensorFlow

https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow

Makoto used the sample TensorFlow code “Deep MNIST for Experts” with minor modifications to the convolution, pooling and last layers, changing the network design to adapt to the pixel format of cucumber images and the number of cucumber classes. https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow

https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow

Typical workflow…

Two phases 1. Learning 2. Inference

learning paradigm Training Data -> Feature Vector -> Learning Algorithm -> Model

inference paradigm “Test” Data -> Feature Vector -> Model -> Prediction

source: Wikipedia - Shoulder of Giants Standing on 
 the shoulders of giants!

Some known DL frameworks

Theano • Python • Easy to learn • Many examples and well known NN available • Difficult for low-level customizations • Hard to debug • Primary developed by Univerity Montreal

Caffe • Python / C++ • Simple interface • Specialized to deal with images • Primarily from Berkley Univeristy

Torch • Lua • Strong support for optimization solvers • Used by Facebook

DL4J - 
 Deeplearning 4 Java • Java / Scala • Spark support for parallelism • Good documentation (now) • Many good examples • Very active community • Works on mobile but slow

Tensorflow • C++ / Python • Growing number of language wrappers, i.e. Java • Works on large-scale GPU as well as Mobile • Very good documentation and example • TensorBoard for visualization • Very fast • Proven to work very well (Google Speech Recognition, Photos, Gmail, …)

Use & 
 re-use existing Models and Networks and Training Data

Transfer Learning

CNN source: https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/


 Example: FaceNet —> Google’s Inception-ResNet-v1 model —> Training set 453.450 images over 
 10.575 identities after face detection

Inference on Mobile!

Training data collection mobile device / desktop / big data server Unlabeled Input data, 
 Training sensors, camera, … data saveTrainingData() upload 
 training data Training API 
 Training API Client

e.g. supervised training mobile device / desktop server Unlabeled Labeled 
 Training Training App Training data data getUnlabeled 
 TrainingData() get 
 saveLabeled 
 unlabeled TrainingData() Training API 
 Training API Client upload 
 labeled

Training on Server server 1. TF performs training on server Training Tensorflow data

Training on Server server 1. TF performs training on server Training Tensorflow data 2. TF saves trained NN saveNN() Trained 
 NN

Training on Server server 1. TF performs training on server Training Tensorflow data 2. TF saves trained NN 3. API provides download of persisted trained NN Trained 
 Training API NN

Inference on Mobile Online mobile device server Input Inference Training Tensorflow data App data fit() Trained 
 Training API NN

Inference on Mobile Offline mobile device server Training Tensorflow data download 
 trained NN Trained 
 Trained 
 Training API NN NN

Inference on Mobile Offline mobile device server Input Inference Training Tensorflow data App data fit() Trained 
 Trained 
 Tensorflow Training API NN NN

Tensorflow on iOS & Android

Getting started… Start CPU only container $ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow Go to your browser on http://localhost:8888/ Start GPU (CUDA) container Install nvidia-docker and run $ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu Go to your browser on http://localhost:8888/

Android https://github.com/tensorflow/tensorflow/tree/master/ tensorflow/contrib/android iOS https://github.com/tensorflow/tensorflow/tree/master/ tensorflow/contrib/ios_examples

Demo iOS Camera Example

Demo Convolutional Networks: from TensorFlow to iOS BNNS https://paiv.github.io/blog/2016/09/25/tensorflow-to-bnns.html https://github.com/paiv/mnist-bnns

Neural Network as a Service

Predicting car prices Source: http://www.datasciencecentral.com/profiles/blogs/predicting-car-prices-part-2-using-neural-network

Deep Learning on Raspberry Pi

How to build a robot that “sees” with $100 and TensorFlow https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow

Takeaways?

Takeaways… 1. Don’t be scared 2. Leverage existing 3. Be a part of the future now

Sources & more info • http://machinelearningmastery.com/deep-learning-for-developers/ • http://www.networkworld.com/article/3025698/microsoft-subnet/ skype-real-time-language-translator-download-windows-ios- android.html • www.datasciencecentral.com/profiles/blogs/predicting-car-prices- part-2-using-neural-network • https://electrek.co/2016/12/21/tesla-autopilot-vision-neural-net-data- elon-musk/ • http://deeplearning4j.org • http://tensorflow.org • https://www.manning.com/books/machine-learning-with-tensorflow

Thank you! Achim Baier 
 Wolfgang Frank @arconsis @wolfgangfrank achim.baier@arconsis.com wolfgang.frank@arconsis.com