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Deep Learning 勉強会/概要

教材を輪読することで、深層学習の基礎や自然言語処理への応用を学びます。

進め方

教材を週ごとに1章ずつ読んでいきます。 発表者は担当の章の要点を説明し、参加者からの質問に答えます。 発表資料を準備する必要はありませんが、発表者と参加者はあらかじめ章の内容を予習しておくことが求められます。

2015

Date
10/26~ 月曜日 16:20 ~ 17:50
Textbook
Deep Learning, Book in preparation for MIT Press- Yoshua Bengio and Ian J. Goodfellow and Aaron Courville URL
Member (発表担当)
島岡,小林,ゼン, 松林

Schedule

ChapterTime担当者参考資料(あれば)備考
Part I: Applied Math and Machine Learning Basics
1.Introduction
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
Part II: Modern Practical Deep Networks
6 Feedforward Deep Networks11/2 (Mon) 16:20 ~ 17:50島岡内部資料+1.Introduction
7 Regularization11/2 (Mon) 16:20 ~ 17:50
8 Optimization for Training Deep Models11/9 (Mon) 16:20 ~ 17:50
9 Convolutional Networks12/16 (Wed) 09:30 ~ 11:00ゼン内部資料
10 Sequence Modeling: Recurrent and Recursive Netsスミット
11 Practical Methodology小林
12 Applications
Part III: Deep Learning Research
13 Structured Probabilistic Models for Deep Learning高瀬
14 Monte Carlo Methods
15 Linear Factor Models and Auto-Encoders
16 Representation Learning
17 The Manifold Perspective on Representation Learning
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models

Last-modified: 2015-12-16 (Wed) 04:06:17 (399d)
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