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Lightning introduction to deep learning, convolutional neural networks, and recurrent neural networks using the translated Stanford CS-230 cheatsheets.
The original original presentation was conducted in English, using the Japanese cheatsheets.

Lightning introduction to deep learning, convolutional neural networks, and recurrent neural networks using the translated Stanford CS-230 cheatsheets.
The original original presentation was conducted in English, using the Japanese cheatsheets.

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Lightning talks: stanford japanese cheetsheets

  1. 1. AI Open Education: Stanford Deep Learning Cheat Sheets in Japanese A brief overview of Deep Learning Kamuela Lau (@kamu_lau) Software Engineer, Consultant at Rondhuit Co., Ltd. October 30, 2019 Code Chrysalis x MLT MiniConf #6
  2. 2. 自己紹介 - Self Introduction ● 株式会社ロンウイット ○ ソフトウェアエンジニア・コンサルタント ● Georgia Institute of Technology に在学中 ○ コンピュータサイエンス・機械学習特化修 士課程 ● Machine Learning Tokyo Contributor ● オープンソース活動 ● 記事執筆 ○ https://codezine.jp/author/1834 ● RONDHUIT Co., Ltd. ○ Software Engineer/Consultant ● Graduate student at Georgia Institute of Technology ○ Computer Science w/ specialization in machine learning ● Machine Learning Tokyo Contributor ● Open source activities ● Article writing ○ https://codezine.jp/author/1834 Kamuela Lau (twitter: @kamu_lau)
  3. 3. Agenda: Stanford 大学 CS-230 資料 Agenda: Stanford University CS-230 Cheatsheets ● アドバイスやコツ ○ ニューラルネットワークの学習 ○ パラメータチューニング ● リカレントニューラルネットワーク ○ 概要 ● 畳み込みニューラルネットワーク ○ 概要 ● Tips and Tricks ○ Training a neural network ○ Parameter tuning ● Recurrent Neural Networks ○ Overview ● Convolutional Neural Networks ○ Overview https://stanford.edu/~shervine/l/ja/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
  4. 4. ニューラルネットワークの学習 その1 Training a Neural Network 1
  5. 5. ニューラルネットワークの学習 その2 Training a Neural Network 2
  6. 6. パラメータチューニング その1 Parameter Tuning 1
  7. 7. パラメータチューニング その2 Parameter Tuning 2
  8. 8. リカレントニューラルネットワークの概要 Overview of Recurrent Neural Networks
  9. 9. 畳み込みニューラルネットワークの概要 Overview of Convolutional Neural Networks
  10. 10. Thank you!

Editor's Notes

  • Good reference for reviewing ML/DL or leading your studies; if there are concepts unknown to you, you can look it up…
    Mentioned worked on the translation for Tips and Tricks
    Will start and focus on Tips and Tricks as it applies to DL in general
    Will briefly talk about RNNs and CNNs
  • Mini-batch Gradient Descent:
    First mention briefly Gradient Descent and Loss/Objective Function
    Loss Function: Difference between output and expected value
    Benefits/drawbacks of gradient on all data vs one point
    Cross Entropy
  • Explain weights
  • NLP, Translation, Word prediction, autocomplete
  • Disclaimer
    Image Processing
  • ×