Machine Learning - Eclipsewiki.eclipse.org/images/7/7b/Machine_Learning_Gentle_Introduction_… ·...
Transcript of Machine Learning - Eclipsewiki.eclipse.org/images/7/7b/Machine_Learning_Gentle_Introduction_… ·...
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Machine Learning «A Gentle Introduction» @ZimMatthias Matthias Zimmermann BSI Business Systems Integration AG
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«What is the difference to IBM’s chess system 20 years ago?»
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AlphaGo Hardware Powered by TPUs
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Tensor Processing Unit (TPU) Specialized ML Hardware
What else is needed?
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Markov Decision Process Environment (Atari Breakout) Agent performing Actions (Left, Right, Release Ball) State (Bricks, location / direction of ball, …) Rewards (A Brick is hit)
Deep Reinforcement Learning
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Q-Learning (simplified)
Markov Decision Process Q(s, a) Highest sum of future Rewards for action a in state s
initialize Q randomly
set initial state s0 repeat
execute a to maximize Q(si, a)
observe r and new state si+1
set Q = update(Q, r, a, si+1)
set si = si+1
until terminated
Deep Reinforcement Learning
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Deep Q Learning (DQN) Q Learning Q(s, a) = Deep Neural Network (DNN) Retrain DNN regularly (using it’s own experience)
Deep Reinforcement Learning
Action a Left, Right, Release
DNN Q(s, a)
State s
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Machine Learning Concepts
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Data Models Training and Evaluation ML Topics
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Challenges Getting the RIGHT data for the task And LOTs of it There is never enough data …
Real World Lessons Data is crucial for successful ML projects Most boring and timeconsuming task Most underestimated task
Getting the Data
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Rosemary, Rosmarinus officinalis
Sentiment Analysis
1245 NEGATIVE \ shallow , noisy and pretentious . 14575 POSITIVE \ one of the most splendid entertainments to emerge from the french film industry in years
Iris or Flower set or example for outlier detection?
86211,B,12.18,17.84,77.79, … 862261,B,9.787,19.94,62.11, … 862485,B,11.6,12.84,74.34, … 862548,M,14.42,19.77,94.48, … 862009,B,13.45,18.3,86.6, …
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Data Models Training and Evaluation ML Topics
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2012, ImageNet, G. Hinton
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Data Models Training and Evaluation ML Topics
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Model Complexity
Training Iterationen
Error Rate
Training Data Test Data
«Underfitting» more training needed
«Overfitting» too much training
model too simple
model too complex
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Data Models Training and Evaluation ML Topics
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Supervised Learning • Learning from Examples • Right Answers are known
Unsupervised Learning • Discover Structure in Data • Dimensionality Reduction
Reinforcement Learning • Interaction with Dynamic Environment
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Demo Time
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Demo 1 Supervised Learning Pattern recognition Handwritten character recognition Convolutional neural network
Demo 2 Unsupervised Learning Natural language processing (NLP) Neural word embeddings Word2vec
Demos
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Data Which digit is this? Collect our own data
Model Deep Neural Network (LeNet-5) Deeplearning4j Deep Learning Library Open Source (Apache) Java
Pattern Recognition Handwritten Digits
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1998 Gradient-based Learing for Document Recognition, Y. LeCun
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Unsupervised Learning Natural Language Processing
Data Google News text training dataset Texts with total of 3’000’000’000 words Lexicon: 3’000’000 words/phrases
Model Word2Vec Skip-gram Mapping: Word 300-dimensional number space Many useful properties (word clustering, syntax, semantics) Deeplearning4j (Train) load and use Google News word2vec model
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Recent Advances
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Games Backgammon 1979, chess 1997, Jeopardy! 2011, Atari games 2014, Go 2016, Poker (Texas Hold’em) 2017 Visual CAPTCHAs 2005, face recognition 2007, traffic sign reading 2011, ImageNet 2015, lip-reading 2016 Other Age estimation from pictures 2013, personality judgement from Facebook «likes» 2014, conversational speech recognition 2016
ML performance >= Human Levels (2017)
https://finnaarupnielsen.wordpress.com/2015/03/15/status-on-human-vs-machines/
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2014, Stanford
http://cs.stanford.edu/people/karpathy/deepimagesent/devisagen.pdf https://gigaom.com/2014/11/18/google-stanford-build-hybrid-neural-networks-that-can-explain-photos/
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2016, UMich+Max Plank
https://arxiv.org/pdf/1605.05396.pdf
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2017, Cornell + Adobe
https://arxiv.org/abs/1703.07511
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2016, Google
https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
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2016, Erlangen, Max-
Plank, Stanford
http://www.graphics.stanford.edu/~niessner/papers/2016/1facetoface/thies2016face.pdf https://www.youtube.com/watch?v=ttGUiwfTYvg
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2011
2015
2016
2017
2014
ML Libraries
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Food for Thought + Next Steps
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?
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Positive Outcomes Statement by Lee Sedol
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Socalizing Go to talks, conferences Visit meetups (Zurich Machine Learning and Data Science, …)
Increase Context Blogs, Twitter, arxiv.org, …
Doing GitHub (deeplearning4j/deeplearning4j, BSI-Business-Systems-Integration-AG/anagnostes, …) Learn Python ;-)
Like to learn more?
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Thanks! @ZimMatthias