best deep learning book

The author Matthew May is a computer postgraduate in parallel machine learning algorithms, and Matthew is also a data mining learner, a data enthusiast, and a dedicated machine-learning scientist. Open source tools play an increasingly important role in data science workflows. GitHub Ten deep learning projects, which i

implementation in Toolbox is very simple:In the NNTRAIN.M:batch_x = batch_x.* (rand (Size (batch_x)) >nn.inputzeromaskedfraction)That is, the size of the (nn.inputzeromaskedfraction) part of the X-0,denoising Autoencoder appears to be stronger than sparse autoencoderContractive auto-encoders:This variant is "Contractive auto-encoders:explicit invariance during feature extraction" proposedThis paper also summarizes a bit of autoencoder, it feels goodThe contractive autoencoders model is:whichThe

Structured Query language to manipulate database.for example:1. INSERT into Events VALUES ("rubyconf", 100); Insert a piece of data into the events table2. SELECT * from events; Take out all the dataTri ACID (4 properties)Transaction: A process of doing business. Package a set of actions to execute together.Use begin;...commit; it can guarantee the correctness of data access, either succeed together or fail together.Atomicity: A transaction is an atom.Consistency: Consistency ensures that the i

Deep Learning, also known as unsupervised feature learning or feature learning, is a hot topic at present. This article mainly introduces the basic idea and common methods of deep learning. 1. What is

21. Application of Depth neural network in visual significance (visual Attention with deep neural Networks) (English, conference papers, 2015, IEEE Search)This article focuses on the application of CNN in the field of significance detection.　　22. Progress in deep learning Research (Chinese, Journal, 2015, net)A summary article on

variables, and the parent process has also seen this modification. The vfork function may occur because the fork of the early system did not implement the write-time replication technology, resulting in a lot of useless work in each fork call (in most cases, it is called exec to execute a new program after fork) the efficiency is not high, so the vfork function is created. The current implementation basically uses the write-time replication technology, and when the vfork function is used improp

1. Why add pooling (pooling) to the convolutional networkIf you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.Advantages of pooling:1. Pooled operation does not increase parameters2. Experimental results show that the model with pooling is more accurateDisadvantages of pooling:1. Because the stride of t

CSS deep understanding of learning notes-margin and css learning notes-margin 1. margin and container size Element size: ① visible size clientWidth (standard); ② occupying size 　　 Margin and visual size: ① applicable to normal block elements without width/height; ② applicable only to horizontal dimension Margin and occupy size: ① block/inline-block horizontal ele

Deep understanding of CSS learning notes border and css learning notes 1. border-width Border-width does not support percentages: semantics and scenarios are determined. In reality, the concepts of borders do not support percentages. Border-width supports keywords: thin, medium (default), and thick. The values are 1px, 3px, and 5px (except IE7 ). Why is the defau

The recent deep learning fire not only attracted the attention of the academic community, but also sought after in the industry. In many important evaluations, DL has achieved the effect of state of the art. Especially in terms of speech recognition, DL has reduced the error rate by about 30% and has made significant progress. If the company that uses speech recognition does not use DL, I am sorry to say he

A summaryIn this paper, we present a very simple image classification deep learning framework, which relies on several basic data processing methods: 1) Cascade principal component Analysis (PCA), 2) Two value hash coding, 3) chunking histogram. In the proposed framework, the multi-layer filter kernel is first studied by PCA method, and then sampled and encoded using two-valued hash coding and block histogr

Deep learning with STRUCTURECharlie Tang is a PhD student in the machine learning group at the University of Toronto, working with Geoffrey Hinton andRuslan Salakhutdinov, whose the interests include machine learning, computer vision and cognitive science. More specifically, he had developed various higher-order extens