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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。

2017

Date
3月30日~ 木曜日 10:00~12:00, 5月11日~ 火曜日 16:20~17:50
Members
松林,松田,横井,栗原,高橋,鶴田,清野,塙

内容

  • 読む本:Deep Learning, Book in preparation for MIT Press- Yoshua Bengio and Ian J. Goodfellow and Aaron Courville URL
  • esaページ

日程・担当

1 Introduction

  • 個々人が頑張って読む

2 Linear Algebra

3 Probability and Information Theory

4 Numerical Computation

5 Machine Learning Basics

6 Feedforward Deep Networks

7 Regularization

8 Optimization for Training Deep Model

9 Convolutional Networks

10 Sequence Modeling: Recurrent and Recursive Nets

11 Practical Methodology

12 Applications

13 Structured Probabilistic Models for Deep Learning

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

過去の記録


Last-modified: 2017-11-13 (Mon) 05:03:15 (4d)
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