松鼠乐园 松鼠乐园
  • 注册
  • 登录
  • 首页
  • 快捷入口
    • Vue
    • Tensorflow
    • Springboot
    • 语言类
      • CSS
      • ES5
      • ES6
      • Go
      • Java
      • Javascript
    • 工具类
      • Git
      • 工具推荐
    • 服务器&运维
      • Centos
      • Docker
      • Linux
      • Mac
      • MySQL
      • Nginx
      • Redis
      • Windows
    • 资源类
      • 论文
      • 书籍推荐
      • 后端资源
      • 前端资源
      • html网页模板
      • 代码
    • 性能优化
    • 测试
  • 重大新闻
  • 人工智能
  • 开源项目
  • Vue2.0从零开始
  • 广场
首页 › 人工智能 › 6000星人气深度学习资源!架构模型技巧全都有

6000星人气深度学习资源!架构模型技巧全都有

迦娜王
3年前人工智能
439 0 0

铜灵 发自 凹非寺

量子位 出品 | 公众号 QbitAI

暑假即将到来,不用来充电学习岂不是亏大了。

有这么一份干货,汇集了机器学习架构和模型的经典知识点,还有各种TensorFlow和PyTorch的Jupyter Notebook笔记资源,地址都在,无需等待即可取用。

除了取用方便,这份名为Deep Learning Models的资源还尤其全面。

针对每个细分知识点的介绍还尤其全面的,比如在卷积神经网络部分,作者就由浅及深分别介绍了AlexNet、VGG、ResNet等。

干货发布后,在GitHub短时间获得了6000 颗星星,迅速聚集起大量人气。

6000星人气深度学习资源!架构模型技巧全都有

图灵奖得主、AI大牛Yann LeCun也强烈推荐,夸赞其为一份不错的PyTorch和TensorFlow Jupyter笔记本推荐!

6000星人气深度学习资源!架构模型技巧全都有

这份资源的作者来头也不小,他是威斯康星大学麦迪逊分校的助理教授Sebastian Raschka,此前还编写过Python Machine Learning一书。

6000星人气深度学习资源!架构模型技巧全都有

话不多说现在进入干货时间,好东西太多篇幅较长,记得先码后看!

原资源地址:

https://github.com/rasbt/deeplearning-models

干货来也

1、多层感知机

多层感知机简称MLP,是一个打基础的知识点:

多层感知机:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-basic.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-basic.ipynb

增加了Dropout部分的多层感知机:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-dropout.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-dropout.ipynb

具备批标准化的多层感知机:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-batchnorm.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-batchnorm.ipynb

从零开始了解多层感知机与反向传播:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-lowlevel.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb

2、卷积神经网络

在卷积神经网络这一部分,细碎的知识点很多,包含基础概念、全卷积网络、AlexNet、VGG等多个内容。来看干货:

卷积神经网络基础入门:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-basic.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-basic.ipynb

卷积神经网络的初始化:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-he-init.ipynb

想用等效卷积层替代全连接的话看看下面这个:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/fc-to-conv.ipynb

全卷积神经网络基础知识在这里:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-allconv.ipynb

Alexnet网络模型在CIFAR-10数据集上的实现:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb

关于VGG模型,你可能需要了解VGG-16架构:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-vgg16.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16.ipynb

在CelebA上训练的VGG-16性别分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb

VGG19网络架构:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg19.ipynb

关于2015年被提出的经典CNN模型ResNet,最厉害的资源也在这了。

比如ResNet和残差块:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/resnet-ex-1.ipynb

用MNIST数据集训练的ResNet-18数字分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb

用人脸属性数据集CelebA训练的ResNet-18性别分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb

在MNIST上训练的ResNet-34:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb

在CelebA上训练ResNet-34性别分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb

在MNIST上训练的ResNet-50数字分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb

在CelebA上训练ResNet-50性别分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb

在CelebA上训练ResNet-101性别分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb

在CelebA上训练ResNet-152性别分类器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb

CIFAR-10分类器中的网络:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10.ipynb

3、指标学习

具有多层感知机的孪生网络:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb

4、自编码器

在自编码器这一部分,同样有很多细分类别需要学习,注意留出充足时间学习这一内容。

自编码器的种类很多,比如全连接自编码器:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-basic.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-basic.ipynb

还有卷积自编码器。比如这个反卷积(转置卷积)卷积自编码器:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-deconv.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv.ipynb

没有进行池化的反卷积自编码器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb

有最近邻插值的卷积自编码器:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-conv-nneighbor.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb

在CelebA上训练过的有最近邻插值的卷积自编码器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb

在谷歌涂鸦数据集Quickdraw上训练过的有最近邻插值的卷积自编码器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb

变分自编码器也是自编码器中的重要一类:

变分自编码器基础介绍:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-var.ipynb

卷积变分自编码器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-var.ipynb

最后,还有条件变分自编码器也需要关注。比如在重建损失中有标签的:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae.ipynb

没有标签的:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb

有标签的条件变分自编码器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb

没有标签的条件变分自编码器:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb

5、生成对抗网络(GAN)

在MNIST上的全连接GAN:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan.ipynb

在MNIST上训练的条件GAN:

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan-conv.ipynb

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv.ipynb

用Label Smoothing方法优化过的条件GAN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv-smoothing.ipynb

6、循环神经网络

针对多对一的情绪分析和分类问题中,包括简单单层RNN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_imdb.ipynb

压缩序列的简单单层RNN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb

RNN和LSTM技术:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb

基于GloVe预训练词向量的有LSTM核的RNN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb-glove.ipynb

GRU核的RNN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb

多层双向RNN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb

一对多/序列到序列的生成新文本的字符RNN:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb

7、有序回归

针对不同场景,有三类有序回归干货:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-coral-afadlite.ipynb

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb

8、方法和技巧

关于周期性学习速率,这里也有一份小技巧:

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/tricks/cyclical-learning-rate.ipynb

9、PyTorch Workflow和机制

用自定义数据集加载PyTorch,这里也有一些攻略:

比如用CelebA中的人脸图像:

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb

比如用街景数据集:

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-svhn.ipynb

比如用Quickdraw:

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb

在训练和预处理环节,标准化图像可参考:

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-standardized.ipynb

图像信息样本:

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb

有文本文档的Char-RNN :

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb

在CelebA上训练的VGG-16性别分类器的并行计算等:

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb

10、TensorFlow Workflow与机制

这是这份干货中的最后一个大分类,包含自定义数据集、训练和预处理两大部分。

内容包括:

将NumPy NPZ用于小批量训练图像数据集

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb

用HDF5文件存储图像数据集,用于小规模训练

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb

用输入pipeline从TFRecords文件中读取数据

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/tfrecords.ipynb

TensorFlow数据集API

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/dataset-api.ipynb

如果需要从TensorFlow Checkpoint文件和NumPy NPZ Archive中存储和加载训练模型,可移步:

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb

11、传统机器学习

最后,如果你是从零开始入门,可以从传统机器学习看起。包括感知机、逻辑回归和Softmax回归等。

感知机部分TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/perceptron.ipynb

PyTorch版笔记

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/perceptron.ipynb

逻辑回归部分也是一样:

逻辑回归部分部分TensorFlow版Jupyter Notebooks

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/logistic-regression.ipynb

PyTorch版笔记

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/logistic-regression.ipynb

Softmax回归,也称为多项逻辑回归:

Softmax回归部分部分TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/softmax-regression.ipynb

PyTorch版笔记

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/softmax-regression.ipynb

传送门

这份干货满满的资源到这里就结束了,再次放上原文传送门:

https://github.com/rasbt/deeplearning-models

超强干货,记得收藏~

0
一文看懂深度学习(白话解释+8个优缺点+4个典型算法)
上一篇
深度学习资源大列表:关于深度学习你需要了解的一切
下一篇
评论 (0)

请登录以参与评论。

现在登录
聚合文章
Servicios profesionales Organizaciones
1年前
在Gitee收获近 5k Star,更新后的Vue版RuoYi有哪些新变化?
1年前
vue3.x reactive、effect、computed、watch依赖关系及实现原理
1年前
Vue 3 新特性:在 Composition API 中使用 CSS Modules
1年前
标签
AI AI项目 css docker Drone Elaticsearch es5 es6 Geometry Go gru java Javascript jenkins lstm mysql mysql优化 mysql地理位置索引 mysql索引 mysql规范 mysql设计 mysql配置文件 mysql面试题 mysql高可用 nginx Redis redis性能 rnn SpringBoot Tensorflow tensorflow2.0 UI设计 vue vue3.0 vue原理 whistle ZooKeeper 开源项目 抓包工具 日志输出 机器学习 深度学习 神经网络 论文 面试题
相关文章
我收集了12款自动生成器,无聊人士自娱自乐专用
输入一张图,就能让二次元老婆动起来,宛如3D:这全是为了科学啊
使用ONNX+TensorRT部署人脸检测和关键点250fps
基于 Keras 的烟火检测
松鼠乐园

资源整合,创造价值

小伙伴
墨魇博客 无同创意
目录
重大新闻 Centos CSS Docker ES5 ES6 Go Java Javascript Linux Mac MySQL Nginx Redis Springboot Tensorflow Vue Vue2.x从零开始 Windows 书籍推荐 人工智能 前端资源 后端资源 壁纸 开源项目 测试 论文
Copyright © 2018-2022 松鼠乐园. Designed by nicetheme. 浙ICP备15039601号-4
  • 重大新闻
  • Centos
  • CSS
  • Docker
  • ES5
  • ES6
  • Go
  • Java
  • Javascript
  • Linux
  • Mac
  • MySQL
  • Nginx
  • Redis
  • Springboot
  • Tensorflow
  • Vue
  • Vue2.x从零开始
  • Windows
  • 书籍推荐
  • 人工智能
  • 前端资源
  • 后端资源
  • 壁纸
  • 开源项目
  • 测试
  • 论文
热门搜索
  • jetson nano
  • vue
  • java
  • mysql
  • 人工智能
  • 人脸识别
迦娜王
坚持才有希望
1224 文章
35 评论
242 喜欢
  • 0
  • 0
  • Top