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A machine learning perspective on neural networks and learning tools

A machine learning perspective on neural networks and learning tools. Tom Schaul. Overview. PyBrain: training artificial neural networks for classification, (sequence) prediction and control Neural networks Modular structure Available architectures Training Supervised learning

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A machine learning perspective on neural networks and learning tools

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  1. A machine learning perspective on neural networks and learning tools Tom Schaul

  2. Overview PyBrain: training artificial neural networks for classification, (sequence) prediction and control • Neural networks • Modular structure • Available architectures • Training • Supervised learning • Optimization • Reinforcement learning (RL) 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  3. Disclaimer • Only version 0.3, you may encounter • inconsistencies • bugs • undocumented “features” • But growing • 10+ contributors • 100+ followers (github, mailing list) • 1000+ downloads 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  4. (Our) Neural Networks No spikes Continuous activations Discrete time steps 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  5. Parameters Derivatives Network Structure: Modules output error output derivatives Module parameters input input error 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  6. Network Structure: Connections output output output output error output error output error Module Module Module FullConnection input input input input error input error input error 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  7. Module Module Module Module Module Module Network Structure:Graphs, Recurrency, Nesting 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  8. Network Components: Modules • Module types • layers of neurons • additive or multiplicative • sigmoidal squashing functions • stochastic outputs • gate units • memory cells (e.g. LSTM cells) • … 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  9. Network Components: Connections • Connection • Fully connected or sparse • Time-recurrent • Weight-sharing • may contain parameters • … 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  10. Network Architectures • Feed-forward networks, including • Deep Belief Nets • Restricted Boltzmann Machines (RBM) • Recurrent networks, including • Reservoirs (Echo State networks) • Bidirectional networks • Long Short-Term Memory (LSTM) architectures • Multi-Dimensional Recurrent Networks (MDRNN) • Custom-designed topologies 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  11. Overview • Neural networks • Modular structure • Available architectures • Training • Supervised learning • Optimization • Reinforcement learning (RL) 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  12. Parameters Derivatives Training: Supervised Learning compare to target output error output derivatives Backpropagation gradientupdate onparameters Module parameters input input error 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  13. Parameters Training: Black-box Optimization BlackBoxOptimizer update parameters fitness parameters Black box fitness function based on e.g. MSE, accuracy, rewards multiple fitness values: multi-objective optimization 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  14. Optimization Algorithms (Stochastic) Hill-climbing Particle Swarm Optimization (PSO) (Natural) Evolution Strategies (ES) Covariance Matrix Adaptation (CMA) Genetic Algorithms (GA) Co-evolution Multi-Objective Optimization (NSGA-II) … 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  15. Training: Reinforcement Learning Environment Environment state action Task Experiment reward observation action Agent 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  16. DataSet Module Learner Explorer reward observation action RL: Agents, Learners, Exploration LearningAgent 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  17. RL: Learning Algorithms and Exploration • Value-based RL • Q-Learning, SARSA • Fitted-Q Iteration • Policy Gradient RL • REINFORCE • Natural Actor-Critic • Exploration methods • Epsilon-Greedy • Boltzmann • State-Dependent Exploration 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  18. RL: Environments and Tasks 2D Mazes (MDP / POMDP) Pole balancing 3D environments (ODE, FlexCube) Board games (e.g. Atari-Go, Pente) 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  19. Also in PyBrain • Unsupervised learning and preprocessing • Support Vector Machines (through LIBSVM) • Tools • Plotting / Visualization • netCDF support • XML read/write support • arac: fast C version 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  20. References • Source download, documentation www.pybrain.org • Mailing list (200+ members) groups.google.com/group/pybrain • Feature requests github.com/pybrain/pybrain/issues • CitationT. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Rückstieß and J. Schmidhuber. PyBrain. Journal of Machine Learning Research, 2010. 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

  21. Acknowledgements Justin Bayer Martin Felder Thomas Rückstiess Frank Sehnke DaanWierstra and many more who contributed code, suggestions, bug fixes … … and you for your attention! 4th FACETS CodeJam Workshop - Tom Schaul - PyBrain

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