最近在学深度学习相关的东西,在网上搜集到了一些不错的资料,现在汇总一下: Free Online Books Deep Learning66 by Yoshua Bengio, Ian Goodfellow and Aaron Courville Neural Networks and Deep Learning42 by Michael Nielsen Deep Learning27 by Mic
Over the last decades, with neural networks and deep learning, several powerful architectures have been proposed, including e.g., convolutional neural networks (CNN), stacked autoencoders, deep Boitzmann machines (DBM), deep generative models and generative adversarial networks (GAN). On the other...
今年2月23日,清华大学计算机系崔鹏副教授与斯坦福大学Susan Athey(美国科学院院士,因果领域国际权威)合作,在世界顶级期刊Nature Machine Intelligence(影响因子IF=15.51,2020)上发表了一篇题为“Stable Learning Establishes Some Common Ground Between Causal Inference and Machine Learning”(稳定学习:建立因果推理和机器学...
Deep learning is a complex machine learning algorithm that involves learning inherent rules and representation levels of sample data through large neural networks with multiple layers. It is popular for its automatic feature extraction capabilities and is applied in various areas such as CNN, LSTM, RN...
To get a baseline, we'll start with a shallow architecture using just a single hidden layer, containing100100 hidden neurons. We'll train for6060 epochs, using a learning rate ofη=0.1η=0.1, a mini-batch size of1010, and no regularization. Here we go**Code for the experiments in this...
《Brief History of Machine Learning》 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机、神经网络、决策树、SVM、Adaboost到随机森林、Deep Learning. 《Deep Learning in Neural Networks: An Overview》 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本《神经网络与深度学习综述》本综述的特点...
《Deep Learning and Shallow Learning》 介绍:对比 Deep Learning 和 Shallow Learning 的好文,来着浙大毕业、MIT 读博的 Chiyuan Zhang 的博客。 《Recommending music on Spotify with deep learning》 介绍:利用卷积神经网络做音乐推荐。 《Neural Networks and Deep Learning》 ...
Deep learning with Python 学习笔记(2) 本节介绍基于Keras的CNN 卷积神经网络接收形状为 (image_height, image_width, image_channels)的输入张量(不包括批量维度),宽度和高度两个维度的尺寸通常会随着网络加深而变小。通道数量由传入 Conv2D 层的第一个参数所控制...
Deep Learning with Pytorch 中文简明笔记 第八章 Using convolutions to generalize 主要内容 1. 卷积 2. 卷积的实现 2.1 卷积的Padding 2.2 卷积的简单理解 2.3 更进一步:深度和池化(pooling) 2.4 整合进神经网络 3. 使用nn.Module来创建模型 4. 训练网络 ...
Recent works have connected deep learning and kernel methods. In this paper, we show that architectural choices such as convolutional layers with pooling, skip connections, make deep learning a composite kernel learning method, where the kernel is a (architecture dependent) composition of base ...