Deep learning: An MIT Press book [推广有奖]

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minnie53053 发表于 2022-8-29 04:41:10 |AI写论文

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Authors: Ian Goodfellow and Yoshua Bengio and Aaron Courville
publisher: MIT Press (2016)

Table of content:
1 Introduction
Part I Applied Math and Machine Learning Basics
2. Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
Part II Deep Networks: Modern Practices
6 Deep Feedforward Networks
7 Regularization for Deep Learning
8 Optimization for Training Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications
Part III Deep Learning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Learning
17 Monte Carlo Methods
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models

Deep learning.rar (24.46 MB, 需要: 5 个论坛币) 本附件包括：
Chapter 20.pdf
Bibliography.pdf
Chapter 2.pdf
Chapter 3.pdf
Chapter 4.pdf
Chapter 5.pdf
Chapter 6.pdf
Chapter 7.pdf
Chapter 8.pdf
Chapter 9.pdf
Chapter 10.pdf
Chapter 11.pdf
Chapter 12.pdf
Chapter 13.pdf
Chapter 14.pdf
Chapter 15.pdf
Chapter 16.pdf
Chapter 17.pdf
Chapter 18.pdf
Chapter 19.pdf
Index.pdf
Intro.pdf
Notation.pdf
Part I.pdf
Part II.pdf
Part III.pdf
TOC.pdf