# \[GN\] (무료 ebook) Little Book of Deep Learning \[143p PDF\]

**URL:** https://discuss.pytorch.kr/t/gn-ebook-little-book-of-deep-learning-143p-pdf/1461
**Category:** 읽을거리&정보공유
**Tags:** ebook, geeknews
**Created:** [5월 2, 2023, 3:32오전 UTC](https://discuss.pytorch.kr/t/gn-ebook-little-book-of-deep-learning-143p-pdf/1461 "2023-05-02T03:32:37Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![9bow](https://discuss.pytorch.kr/user_avatar/discuss.pytorch.kr/9bow/32/16301_2.png) [@9bow](https://discuss.pytorch.kr/u/9bow)
#### Post date: [5월 2, 2023, 3:32오전 UTC](https://discuss.pytorch.kr/t/gn-ebook-little-book-of-deep-learning-143p-pdf/1461/1 "2023-05-02T03:32:37Z")

</div>

_[GeekNews](https://news.hada.io/)의 [xguru](https://news.hada.io/user?id=xguru)님께 허락을 받고 GN에 올라온 글들 중에 AI 관련된 소식들을 공유하고 있습니다. 😺_

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### 소개

- 작은(Little) 모바일 기기의 화면에서 읽기 좋게 편집된 François Fleure 교수의 딥러닝 기초 서적

[![image](https://discuss.pytorch.kr/uploads/default/original/2X/9/9f63c6af787f88ded334de39fcb0fdbf231a5d1c.jpeg)](https://fleuret.org/public/lbdl.pdf)

## I. Foundations

1. Machine Learning  
1.1 Learning from data  
1.2 Basis function regression  
1.3 Under and over-fitting  
1.4 Categories of models
2. Efficient Computation  
2.1 GPUs, TPUs, and batches  
2.2 Tensors
3. Training  
3.1 Losses  
3.2 Autoregressive models  
3.3 Gradient descent  
3.4 Backpropagation  
3.5 Training protocols  
3.6 Training data

## II. Deep Models

1. Model Components  
4.1 The notion of layer  
4.2 Linear layers  
4.3 Activation functions  
4.4 Pooling  
4.5 Dropout  
4.6 Normalizing layers  
4.7 Skip connections  
4.8 Attention layers  
4.9 Token embedding  
4.10 Positional encoding
2. Architectures  
5.1 Multi-Layer Perceptrons  
5.2 Convolutional networks  
5.3 Attention models

## III. Applications

1. Prediction  
6.1 Image denoising  
6.2 Image classification  
6.3 Object detection  
6.4 Semantic segmentation  
6.5 Speech recognition  
6.6 Text-image representations
2. Synthesis  
7.1 Text generation  
7.2 Image generation

### 원문 (eBook 다운로드)

> **[lbdl.pdf](https://fleuret.org/public/lbdl.pdf)**
>
> 4.52 MB

### 출처 / GeekNews

> **[Little Book of Deep Learning \[143p PDF\] | GeekNews](https://news.hada.io/topic?id=9085)**
>
> 작은(Little) 모바일 기기의 화면에서 읽기 좋게 편집된 François Fleure 교수의 딥러닝 기초 서적I. FoundationsMachine Learning1.1 Learning from data1.2 Basis function regression1.3 Under and over-fitting1.4 Categories of modelsEffici