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Machine Learning: A gentle Introduction

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Matthias Zimmermann
Matthias Zimmermann

Over the last years machine learning has become a hot topic. This is no surprise as the state of the art has reached levels that started to gain a lot of attention in the media: Self-driving cars, winning to Go game against top professional players and social media platforms that recognize your face in most online pictures. This presentation provides an overall picture of machine learning and does not require any previous knowledge in the field. Sit back, relax and learn more about key concepts and recent advances of the field. The demo part looks at two specific examples * Pattern recognition with handwritten character recognition * Natural language processing with neural word embeddings At the end some less well known examples/topics will help to illustrate the current state of the art and should provide some food for thought regarding the future directions of this domain. The slides have been used for a session with the Java User Group Switzerland: https://www.jug.ch/html/events/2017/machine_learning.html

Tecnologia
1 de 41
Machine Learning «A Gentle Introduction» @ZimMatthias Matthias Zimmermann BSI Business Systems Integration AG
«What is the difference to IBM’s chess system 20 years ago?»
AlphaGo Hardware Powered by TPUs
Tensor Processing Unit (TPU) Specialized ML Hardware What else is needed?
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
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
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
Machine Learning Concepts
Data Models Training and Evaluation ML Topics
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
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, …
Data Models Training and Evaluation ML Topics
2012, ImageNet, G. Hinton
Data Models Training and Evaluation ML Topics
Model Complexity Training Iterationen Error Rate Training Data Test Data «Underfitting» more training needed «Overfitting» too much training  model too simple model too complex
Data Models Training and Evaluation ML Topics
Supervised Learning • Learning from Examples • Right Answers are known Unsupervised Learning • Discover Structure in Data • Dimensionality Reduction Reinforcement Learning • Interaction with Dynamic Environment
Demo Time
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
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
1998 Gradient-based Learing for Document Recognition, Y. LeCun
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
Recent Advances
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/
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/
2016, UMich+Max Plank https://arxiv.org/pdf/1605.05396.pdf
2017, Cornell + Adobe https://arxiv.org/abs/1703.07511
2016, Google https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
2016, Erlangen, Max- Plank, Stanford http://www.graphics.stanford.edu/~niessner/papers/2016/1facetoface/thies2016face.pdf https://www.youtube.com/watch?v=ttGUiwfTYvg
2011 2015 2016 2017 2014 ML Libraries
Food for Thought + Next Steps
?
Positive Outcomes Statement by Lee Sedol
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?
Thanks! @ZimMatthias

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Machine Learning: A gentle Introduction

Notas do Editor

  1. You have probably followed the story of the korean wold champion in go loosing against the alphago system built by the guys at deep mind. So, how is playing go different to playing chess from a systems perspective? This is nicely explained in the following video clip The next clip has «demis hassabis» from deepmind taking about the difference of playing go and chess from a human perspective Video 1: What is go and how does it compare to chess? Video 2: As the complexity of go is so much higher than with chess, intuition becomes even more important. Video 3: The last snippet is a high level description of the training of alpha go
  2. But of course – as in the case of the chess playing system – there is also a hardware story behind the win of alphago --- AlphaGo was powered by TPUs in the matches against Go world champion, Lee Sedol, enabling it to "think" much faster and look farther ahead between moves. https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html?utm_content=buffer73148&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
  3. The Tensor Processing Units (TPU) are custom built ASIC boards that boost machine learning applications by parallelizing large amounts of low precision matrix computations. This brings around 3 generation of moore’s law making available the processing power today that we could expect to be available 6 years in the future … https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html?utm_content=buffer73148&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
  4. Deep reinforcement learning was used for the Alpha go A milestone article about Atari games was submitted to Nature just 2 years ago The Alpha-Go Paper https://deepmind.com/research/dqn/ http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Deep reinforcement learning https://www.nervanasys.com/demystifying-deep-reinforcement-learning/ http://rll.berkeley.edu/deeprlcourse/ tensor flow: https://www.tensorflow.org/
  5. https://www.nervanasys.com/demystifying-deep-reinforcement-learning/
  6. https://www.nervanasys.com/demystifying-deep-reinforcement-learning/
  7. https://github.com/tambetm/simple_dqn Agent implementation https://github.com/tambetm/simple_dqn/blob/master/src/agent.py DQN implementation https://github.com/tambetm/simple_dqn/blob/master/src/deepqnetwork.py https://www.nervanasys.com/openai/ https://github.com/NervanaSystems/neon
  8. In the follwing we will Get some stories about where all this came from Talk about the major concepts Do some small demos And have a look at current work that provides a glimpse at the things to come
  9. Most boring task and timeconsuming task of most machine learning projects (often > most of the time of a new machine learning project is used to get at the right data and enough data) Most underestimated task But, Good data sets are valuable over many decades
  10. movie reviews: sentiment polarity http://www.cs.cornell.edu/People/pabo/movie-review-data/ Handwritten digits: mnist http://yann.lecun.com/exdb/mnist/ breast cancer: wdbc https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) imagenet competition http://image-net.org/challenges/LSVRC/2012/ http://image-net.org/challenges/LSVRC/2012/browse-synsets http://image-net.org/synset?wnid=n12864160 (rosemary) many more data sets http://deeplearning.net/datasets/
  11. Massive revival with deep deep learning Classical neural networks Many layers Some tricks Rebranding from neural nets to deep learning
  12. more training needed overtrained
  13. http://cs229.stanford.edu/schedule.html
  14. In the follwing we will Get some stories about where all this came from Talk about the major concepts Do some small demos And have a look at current work that provides a glimpse at the things to come
  15. Convolutinoal nn explained http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/?utm_content=bufferfb698&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer http://yann.lecun.com/exdb/mnist/ https://deeplearning4j.org/
  16. web online demo https://transcranial.github.io/keras-js/#/mnist-cnn
  17. Word2vec model https://github.com/mmihaltz/word2vec-GoogleNews-vectors 3 billion words, 3 million vocabular (incl phrases such as «boston globe») http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ 100 billion words from a Google News dataset Original paper (mikolov et al, 2013a) «Efficient Estimation of Word Representations in Vector Space” vector dimensionality 300 and context size 5 https://arxiv.org/pdf/1301.3781.pdf [6 billion tokens, 1 million words in voc] 2nd paper (mikolov et al, 2013b) https://arxiv.org/pdf/1310.4546.pdf []
  18. https://finnaarupnielsen.wordpress.com/2015/03/15/status-on-human-vs-machines/ https://www.researchgate.net/publication/220271810_Computers_beat_Humans_at_Single_Character_Recognition_in_Reading_based_Human_Interaction_Proofs_HIPs
  19. Also in 2014, researchers started to combine pattern recognition and natural language processing, both based on deep learning Stanford (2014) http://cs.stanford.edu/people/karpathy/deepimagesent/devisagen.pdf Google + stanford (2014) https://gigaom.com/2014/11/18/google-stanford-build-hybrid-neural-networks-that-can-explain-photos/
  20. https://arxiv.org/pdf/1605.05396.pdf
  21. https://arxiv.org/abs/1703.07511
  22. https://research.googleblog.com/2016/09/a-neural-network-for-machine.html paper https://arxiv.org/abs/1609.08144 Newer version (multi-lingual) https://research.googleblog.com/2016/11/zero-shot-translation-with-googles.html
  23. face2face http://www.graphics.stanford.edu/~niessner/thies2016face.html video https://www.youtube.com/watch?v=ttGUiwfTYvg paper http://www.graphics.stanford.edu/~niessner/papers/2016/1facetoface/thies2016face.pdf
  24. Theano (python) http://deeplearning.net/software/theano/ (2011) Tensorflow (python) https://www.tensorflow.org/ (2015) Keras (Python) https://keras.io/ (2016) Pytorch (python) http://pytorch.org/ (2017) Deeplearning4j (Java) https://deeplearning4j.org/ (2014)
  25. Financial times: https://www.ft.com/content/063c1176-d29a-11e5-969e-9d801cf5e15b Medium: https://medium.com/basic-income/deep-learning-is-going-to-teach-us-all-the-lesson-of-our-lives-jobs-are-for-machines-7c6442e37a49#.w1d0sk8dn
  26. https://www.stlouisfed.org/on-the-economy/2016/january/jobs-involving-routine-tasks-arent-growing 1. The 90’ Computers getting used everywhere, first industrial roboters (? Need to verify!) 2. 2008 Financial crisis 3. 2017+ Future …
  27. arXiv https://arxiv.org/list/stat.ML/current Twitter @StanfordNLP, @OpenAI ‏ @AndrewYNg, @goodfellow_ian, @karpathy, @hugo_larochelle ‏, @ylecun ‏, @dennybritz ‏, @StatMLPapers ‏