![Deep Neural Network (DNN)
• ✄ ✁✂☎ ✆Neural Network (NN) ✝ !
- NN ✞ ✟✠✡☛☞✌✍✠ ✎ ✏✑✒✠!
- W. McCulloch ✓W. Pitts ✔ ✎ ✕✖(1943) [2]
• ✆Deep Learning ✗ ✗DNN ✝ ✘ ✙✚!
✝ ✛✗ ✘( ✙ )
✜1✢(2016/2/27) MPS Yokohama ✣✤✥✦✧★✩✪(c) Junya Kaneko](https://image.slidesharecdn.com/mpsy20160227dist-160301142930/85/Deep-Learning-with-Python-Vol-1-11-320.jpg)
![Deep Learning
• Android OS ✄ (Google Brain Project)
• Google ✄ Photo Search [3]
• FB ✄ (DeepFace) [4]
• ✟ [5]
☛1✆(2016/2/27) MPS Yokohama ☞ ✁✂☎✝✞✠(c) Junya Kaneko](https://image.slidesharecdn.com/mpsy20160227dist-160301142930/85/Deep-Learning-with-Python-Vol-1-13-320.jpg)
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数学的基礎から学ぶ Deep Learning (with Python) Vol. 1
- The document summarizes a presentation on deep learning and neural networks given by Junya Kaneko of MPS Yokohama. - It introduces key concepts of deep learning, neural networks, and TensorFlow for deep learning. - It provides examples of applications of deep learning like in Android OS, Google Photos, Facebook's DeepFace, and self-driving cars. - The presentation demonstrates how to set up TensorFlow on a virtual machine and build simple neural networks.
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数学的基礎から学ぶ Deep Learning (with Python) Vol. 1
- 1. MPS Yokohama ☛1✆(2016/02/27)✄ ✁✂☎✝ Deep Learning (with Python) Vol. 1 Morning Project Samurai ✞✟ ✠✡☞✌ ✍1✎(2016/2/27) MPS Yokohama ✏✑✒✓✔✕✖✗(c) Junya Kaneko
- 2. • ✍ ✁ ✂ •Deep Learning ✆ • Neural Network ✆ • TensorFlow ✄Deep Learning ☎ ✝✞✟! • ✠✡☛☞ ✌1✎(2016/2/27) MPS Yokohama ✏✑✒✓✔✕✖✗(c) Junya Kaneko
- 3. MPS • Morning! - ☞ ✁✂✄☎✆✝✞ •Project! - ✟✠✡☛✌✍✎✏ • Samurai! - ✑✒✞✓✔! - ✟✠✡☛✌✍✄✕✖✗✕✖✗! ✘✙✚✛✜✢ ✣✤✥✦✧★ ✩✪✫✪✬✭✣ ✮✯✰✪✬✭✣ ✱✲ ✳✴ UP! UP! UP! ✵✶✷✸ ✹✺✻✼✽ UP! UP! ✾1✿(2016/2/27) MPS Yokohama ❀❁❂❃❄❅❆❇(c) Junya Kaneko
- 5. Why Deep Learning? • • Mindof Engineers ☛1✆(2016/2/27) MPS Yokohama ☞ ✁✂✄☎✝✞(c) Junya Kaneko
- 6. ✂✂✂ 2 ✆3 ✎ ✄ ✁✁ ☎ ✝✞✟✠✡☛✞ Facebook: http://bit.ly/mpsfbgroup Twitter ☞✌✍✏✑ : #mpsamurai ✒1✓(2016/2/27) MPS Yokohama ✔✕✖✗✘✙✚✛(c) Junya Kaneko
- 7. Python !! ☛1✆(2016/2/27) MPSYokohama ☞ ✁✂✄☎✝✞(c) Junya Kaneko
- 9. • ✍ ✁✂✄☎ • DeepLearning ✆ • Neural Network ✝ • TensorFlow ✞Deep Learning ✟ ✠✡☛! • ☞✌✎✏ ✑1✒(2016/2/27) MPS Yokohama ✓✔✕✖✗✘✙✚(c) Junya Kaneko
- 11. Deep Neural Network(DNN) • ✄ ✁✂☎ ✆Neural Network (NN) ✝ ! - NN ✞ ✟✠✡☛☞✌✍✠ ✎ ✏✑✒✠! - W. McCulloch ✓W. Pitts ✔ ✎ ✕✖(1943) [2] • ✆Deep Learning ✗ ✗DNN ✝ ✘ ✙✚! ✝ ✛✗ ✘( ✙ ) ✜1✢(2016/2/27) MPS Yokohama ✣✤✥✦✧★✩✪(c) Junya Kaneko
- 13. Deep Learning • AndroidOS ✄ (Google Brain Project) • Google ✄ Photo Search [3] • FB ✄ (DeepFace) [4] • ✟ [5] ☛1✆(2016/2/27) MPS Yokohama ☞ ✁✂☎✝✞✠(c) Junya Kaneko
- 14. Deep Learning • ✝ ! - ✄✁✂ ☎✆ ✞ ✟✠✡☛☞✆ • ✌ ! - ✄✁✂ ☎✆ ✞ ✟✠✡☛☞✍☞ ! - ✎✏ ✑✒ ✓1✔(2016/2/27) MPS Yokohama ✕✖✗✘✙✚✛✜(c) Junya Kaneko
- 15. Deep Learning • ✝ ! - ✄✁✂ ☎✆ ✞ ✟✠✡☛☞✆ • ✌ ! - ✄✁✂ ☎✆ ✞ ✟✠✡☛☞✍☞ ! - ✎✏ ✑✒ ✓✔✕✖✗ ✘1✙(2016/2/27) MPS Yokohama ✚✛✜✢✣✤✥✦(c) Junya Kaneko
- 16. • ✍ ✁✂✄☎ • DeepLearning ✆ • Neural Network ✝ • TensorFlow ✞Deep Learning ✟ ✠✡☛! • ☞✌✎✏ ✑1✒(2016/2/27) MPS Yokohama ✓✔✕✖✗✘✙✚(c) Junya Kaneko
- 17. ✄ y0 0 w0,0 w0,1 w0,2 x0 x1 x2 f ☛1✆(2016/2/27) MPS Yokohama☞ ✁✂☎✝✞✟(c) Junya Kaneko
- 18. NN 1 2 0x0 x1 x2 y0 f f f y1 y2 wi,j ☛1✆(2016/2/27) MPS Yokohama☞ ✁✂✄☎✝✞(c) Junya Kaneko
- 20. • ✍ ✁✂✄☎ • DeepLearning ✆ • Neural Network ✆ • TensorFlow ✝Deep Learning ✞ ✟✠✡! • ☛☞✌✎ ✏1✑(2016/2/27) MPS Yokohama ✒✓✔✕✖✗✘✙(c) Junya Kaneko
- 22. NN softmax 1 2 0x0 x1 x2 y0 y1 y2 wi,j softmax softmax (784✟ ) (10✟ ) ☛1✆(2016/2/27) MPSYokohama ☞✁✂✄☎✝✞✠(c) Junya Kaneko
- 23. TensorFlow Deep Learning! ! ( ) 1. USB ✄Virtual Box ✁✂☎✆✝ ✞PC ✟✠✡✝ 2. Virtual Box ✂☎☛☞✌✝✍ 3. ☎✆✝ ✂☎☛ ✝✌ 4. Virtual Box ✂ 5. ✎✏✑ ✄192.168.33.10:8888 ✟✒✓✔☞ ✕1✖(2016/2/27) MPS Yokohama ✗✘✙✚✛✜✢✣(c) Junya Kaneko
- 26. • Placeholder: ✄ ✁✂☎ ✆ • Variable: • nn: ✝✞✄✟ ✠✡☛✄☞ • matmul: ✌ ✍ ✎1✏(2016/2/27) MPS Yokohama ✑✒✓✔✕✖✗✘(c) Junya Kaneko
- 27. • ✍ ✁✂✄☎ • DeepLearning ✆ • Neural Network ✆ • TensorFlow ✝Deep Learning ✞ ✟✠✡! • ☛☞✌ ✎1✏(2016/2/27) MPS Yokohama ✑✒✓✔✕✖✗✘(c) Junya Kaneko
- 28. ✂ ✁ ✄☎ - Activation function - Step - Sigmoid - Softmax! - Cost function! - MSE (Mean Squared Error) - Cross-Entropy ☛1✆(2016/2/27) MPS Yokohama ☞✝✞✟✠✡✌✍(c) Junya Kaneko
- 29. 1. Neural networksand deep learning. ! http://neuralnetworksanddeeplearning.com/chap6.html 2. Neural Networks! https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/ History/history1.html 3. Improving Photo Search: A Step Across the Semantic Gap! http://googleresearch.blogspot.jp/2013/06/improving-photo-search-step-across.html 4. DeepFace: Closing the Gap to Human-Level Performance in Face Verification! http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/ Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf 5. Here’s How Deep Learning Will Accelerate Self-Driving Cars! http://blogs.nvidia.com/blog/2015/02/24/deep-learning-drive/ 6. COS 511: Theoretical Machine Learning. ! http://www.cs.princeton.edu/courses/archive/spr08/cos511/scribe_notes/0204.pdf 7. MNIST For ML Beginners. ! https://www.tensorflow.org/versions/r0.7/tutorials/mnist/beginners/index.html 8. Deep MNIST for Experts.! https://www.tensorflow.org/versions/r0.7/tutorials/mnist/pros/index.html☛1✆(2016/2/27) MPS Yokohama ☞ ✁✂✄☎✝✞(c) Junya Kaneko