Lightning talks: stanford japanese cheetsheets
•0 likes•1,548 views
Report
Share
Download to read offline
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.
More Related Content
Similar to Lightning talks: stanford japanese cheetsheets
Similar to Lightning talks: stanford japanese cheetsheets(20)
Recently uploaded
Recently uploaded(7)
Lightning talks: stanford japanese cheetsheets
- 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. 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. 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. ニューラルネットワークの学習 その１ Training a Neural Network 1
- 5. ニューラルネットワークの学習 その２ Training a Neural Network 2
- 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. 畳み込みニューラルネットワークの概要 Overview of Convolutional Neural Networks
- 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