2nd DL Meetup @ Dublin - Irene
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2nd DL Meetup @ Dublin: DL Apps, TensorFlow and useful links.
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2nd DL Meetup @ Dublin - Irene
- 1. TENSORFLOW & DEEP LEARNING Irene Li PhD Student @ DIT RA @ Barcelona Supercomputer Centre
- 2. OUTLINE • Deep Learning Applications • TensorFlow - Introduction - Benefit - Demo • Materials
- 3. DL TOPICS Image recognition Speech recognition Video captioning NLP word embedding, Translation Biological model… Neural Arts Games Self-Driving Cars Deep Residual Learning GPUs
- 4. NEURAL ART A Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
- 7. NEURAL ART
- 8. CNN FOR CV
- 9. VGG MODEL
- 10. NEURAL ART – STYLE GENERATION Code by Mark Chang, National Taiwan University https://github.com/ckmarkoh
- 12. GAMES – DEEP Q-NETWORK (DQN) https://deepmind.com/dqn.html Deep Neural Network + Reinforcement Learning how the game agents should act in an environment in order to maximize future cumulative reward (e.g., a game score)
- 13. GAMES – DEEP Q-NETWORK (DQN) Naveen Appiah, Sagar Vare, Stanford. Playing FlappyBird with Deep Reinforcement Learning Develop a CNN model to learn features from snapshots of the game and train the agent to take the right actions at each game instance.
- 14. GAMES – DEEP Q-NETWORK (DQN) Naveen Appiah, Sagar Vare, Stanford. Playing FlappyBird with Deep Reinforcement Learning Deep neural network to learn game specific features from raw pixels and decide on what actions to take. Instead, a reinforcement learning setting tries to evaluate the actions at a given state based on the reward it observes by executing it.
- 16. SELF-DRIVING CARS: NVIDIA DRIVE PX • Dual NVIDIA Tegra® X1 processors delivering a combined 2.3 Teraflops • Interfaces for up to 12 cameras, radar, lidar, and ultrasonic sensors • Rich middleware for graphics, computer vision, and deep learning
- 20. Object Detection
- 22. RACING CARS: NVIDIA DRIVE PX2
- 23. DEEP RESIDUAL NETWORK FOR IMAGE RECOGNITION Microsoft Research: • ImageNet computer vision challenge championship 3.57% error on the ImageNet test set. • 152 layers of deep networks, 8 times deeper than VGG • Image classification, detection and localization Deep Residual Learning for Image Recognition http://arxiv.org/pdf/1512.03385v1.pdf
- 24. NVIDIA DGX-1: WORLD’S FIRST DL SUPERCOMPUTER Eight Tesla P100 GPU accelerators, 16GB memory per GPU
- 25. TENSORFLOW 2ed Generation of Distributed ML Systems
- 26. DL FRAMEWORKS TensorFlow: Google MXNET: dmlc Theano: LISA Torch: Facebook, DeepMind CNTK: Microsoft Caffe: Berkley, Google
- 28. TENSORFLOW: OPEN SOURCE DL SYSTEM • Core in C++. • Supports Python and C++. Jeff Dean & Oriol Vinyals Google, NIPS, 2015
- 29. COMPUTATION: A DATAFLOW GRAPH • Edges: Flow of tensors. • Graph of Nodes: Operations. Jeff Dean & Oriol Vinyals Google, NIPS, 2015
- 30. DISTRIBUTED • Multiple Devices! • Send and Receive Nodes Jeff Dean & Oriol Vinyals Google, NIPS, 2015
- 32. EASY LIFE Everything is in Tensor Softmax Function https://www.tensorflow.org/versions/r0.7/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners x b W Matmul Add Softm ax
- 33. EASY LIFE Gradient Descent: Initialization & launch https://www.tensorflow.org/versions/r0.7/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners
- 34. A FEW TENSORFLOW COMMUNITY EXAMPLES ● DQN: github.com/nivwusquorum/tensorflow-deepq ● NeuralArt: github.com/woodrush/neural-art-tf ● Char RNN: github.com/sherjilozair/char-rnn-tensorflow ● Keras ported to TensorFlow: github.com/fchollet/keras ● Show and Tell: github.com/jazzsaxmafia/show_and_tell.tensorflow ● Mandarin translation: github.com/jikexueyuanwiki/tensorflow-zh
- 35. USEFUL LINKS Want to master DL?
- 36. NNDL • SGD • BP • REGULARIZATION • DROPOUT • … http://neuralnetworksanddeeplearning.com/
- 38. REINFORCEMENT LEARNING • RICHARD SUTTON • ANDREW BARTO
- 39. 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) • HTTP://WWW0.CS.UCL.AC.UK/STAFF/D.SILVER/WEB/TEAC HING.HTML (RL)
- 40. TENSORFLOW • GOOGLE OPEN TENSORFLOW IN LAST YEAR • HTTP://TENSORFLOW.ORG/ • HTTPS://GITHUB.COM/TENSORFLOW/TENSORFLOW • HTTP://DOWNLOAD.TENSORFLOW.ORG/PAPER/WHITEPAPER20 15.PDF
- 41. UDACITY COURSE ON TF • HTTPS://WWW.UDACITY.COM/COURSE/DEEP-LEARNING-- UD730 • HTTPS://GITHUB.COM/TENSORFLOW/TENSORFLOW/TREE/M ASTER/TENSORFLOW/EXAMPLES/UDACITY http://ireneli.eu/2016/03/13/tensorflow-04-implement-a-lenet-5-like-nn-to-classify-notmnist-images/
- 43. REFERENCES • HTTP://INDICO.CERN.CH/EVENT/510372/ATTACHMENTS/1245509/1840815/LECUN-20160324- CERN.PDF • HTTP://WWW.JIANSHU.COM/NOTEBOOKS/339523/LATEST • HTTP://PEOPLE.IDSIA.CH/~JUERGEN/BLUES/IDSIA-07-02.PDF • HTTP://DEEPDREAMGENERATOR.COM/ • HTTPS://GITHUB.COM/PHANEIN/DEEPWALK • HTTP://WWW.CS.TORONTO.EDU/~RSALAKHU/PAPERS/ANNREV.PDF • HTTP://WWW.JIANSHU.COM/P/8799AFD9B7C7 • HTTP://WWW.JIANSHU.COM/P/10D70C5CEB39
Editor's Notes
- These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.
- This allows algorithms to accurately understand the full 360 degree environment around the car to produce a robust representation, including static and dynamic objects. Use of Deep Neural Networks (DNN) for the detection and classification of objects dramatically increases the accuracy of the resulting fused sensor data. - See more at: http://www.nvidia.com/object/drive-px.html#sthash.Vq3ZHH11.dpuf
- This allows algorithms to accurately understand the full 360 degree environment around the car to produce a robust representation, including static and dynamic objects. Use of Deep Neural Networks (DNN) for the detection and classification of objects dramatically increases the accuracy of the resulting fused sensor data. - See more at: http://www.nvidia.com/object/drive-px.html#sthash.Vq3ZHH11.dpuf
- Under circustanes
- Under circustanes
- 150 Macbook Pro
- The combination of these software capabilities running on Pascal-powered Tesla GPUs allows applications to run 12x faster than with previous GPU-accelerated solutions.