Deep Learning Shanghai

Kick off!

Goal

• Help people get introduced into DL

• Help investors find potential projects

• Utilize wonderful techniques to solve problems

Schedule

• Deep Learning frameworks and research

• Products that already utilize DL

• Chat-robots and Q&A system

Who I am

• Now data scientist in jianshu.com

• work on recommender system

• research on social network analysis and game theory (ICTAI 2012)

• recently get involved in deep learning on NLP and social network analysis

Part One

• techniques

• products

• theory

What is the Deep Learning!?

• simply another name for Neural Networks

• incorporate new thinkings

• heavy computation resources consumer

borrowed from Stanford Seminar - Oriol Vinyals of Google

Deep Learning• Books:

• http://www.deeplearningbook.org/

• neuralnetworksanddeeplearning.com

• Survey Papers:

• http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf

Deep Learning

NNDL

Review of DL

online courses

• https://www.youtube.com/user/hugolarochelle (python)

• https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ (torch)

• https://www.coursera.org/course/neuralnets (matlab)

DL Frameworks• TensorFlow: Google

• MXNET: dmlc

• theano: LISA

• torch: Facebook, DeepMind

• CNTK: Microsoft

• caffe: Berkley, Google

Get it works!

• To gain a better understanding

• mathematics

• programming

TensorFlow

• Google open tensorflow in last year

• http://tensorflow.org/

• https://github.com/tensorflow/tensorflow

• http://download.tensorflow.org/paper/whitepaper2015.pdf

Whitepaper

Udacity course on tf

• https://www.udacity.com/course/deep-learning--ud730

• https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/udacity

TensorFlow

• http://wiki.jikexueyuan.com/project/tensorflow-zh/?hmsr=www_index_wiki

MXNET

• https://github.com/dmlc/mxnet

Transforming How We Diagnose Heart Disease

• https://github.com/dmlc/mxnet/tree/master/example/kaggle-ndsb2

torch

• http://torch.ch/

• https://github.com/torch/torch7/wiki/Cheatsheet

Example

• http://torch.ch/docs/cvpr15.html

cltorch

• support OpenCL

• can use intel video card to do dl

• https://github.com/hughperkins/cltorch

Comparisons of these frameworks

• https://github.com/zer0n/deepframeworks/blob/master/README.md

Try them

• get a proper machine to start!

• GPUs are better

Recent advances in DL

• http://web.stanford.edu/class/ee380/Abstracts/160127.html

Research work

• Welcome Pai Peng to give a talk about his work published in CIKM '15

• Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

About Pai Peng

• Phd Candidate Pai Peng from ZJU

• DeepCamera: A Unified Framework for Recognizing Places-of-Interest based on Deep ConvNets. CIKM 2015

arts

Play games

products

• StyleAI

• Caiyun

• neuralstyle

• ����

Part 2

Your problems• jack: deep learning.

• vivian: social network market, NLP. Advertisement.

• xiande: robotic controller. platform develop

• frank pei: big data, machine learning, financial, risk model, fraud detection

• lillie: financial

• kai: tumor data medince

• bonnie: accounting finance

• John: data analyze, application of big data, environment data,… Rapid Miner. Boston ,DM..Germany.

• Wendy: build solutions,

• Yana: data scientist, not only solutions, airline. physical engineering interconnections. aircraft data.

• John Zhang: sales service.

Your problems• Anson: RapidMiner China, deep learning extension, DL4J

• Nathan: ads platform, MediaV => MVAD, machine learning. cookies, 360 give data. clusters computing resources.

• Theo: France. web developer.

• Nancy: anthropology, programming.

• Colin: government company, product inspection, deep learning

Your problems• objects detection, image, transportation beijing city subway, bus, taxi,

smart card, gps, machine learning=>deep learning models for video.

• kawo.com: NLP, topic extraction, combination lda, tfidf, suggestion for tagging, which time.

• NirVa: Hinton 2007 paper, bloom to huge areas, potential application, design for human being, feel beautiful things, graphical music.

• RM: social media, fraud detection,human resources … transportation

• JFPAL: abnormal detection gmm model to solve, traditional methods maybe not work, disease detection, unsupervised way, distant learning. semi supervised way. manifold learning. graphical model. frequent subgraph. TKDE 2009 learning imbalanced data. samplings. kernel active learning.

Part 3• QA systems

• chat robot

• produce dl semantic answer

• FAQ corpus, presale aftersalue, knowledge graph. map to key phrase into answer.

• 9 fraud detect, 7 social topic extraction, 7 image.

potential Solutions

To summarize

• techniques

• problems

• solutions

Deep Learnng for

Better Future

THE END