deep learning nanodegree price

). 　　The autoencoder of Sparse Coding explains: First, let's take a look at the LK norm number of vector X. Its value is: From this we can see that the l1 norm is the sum of each element, and the L2 norm is the Euclidean distance from the vector to the far point. The price function for expressing Sparse Coding in the form of a matrix is as follows: As mentioned above, the base value S is also subjected to sparse penalty, which is constrained by L1

Objective: This article is mainly to practice multivariable linear regression problem (in fact, this article also on 3 variables), reference page: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course= Deeplearningdoc=exercises/ex3/ex3.html. In fact, in the previous blog Deep learning: Two (linear regression practice) the solution of one-element linear regression problem is briefly introduced

A Neural Network approach to context-sensitive Generation of conversational responsesLeverage Financial News to Predict the Stock price movements Using Word embeddings and deep neural NetworksMatchnet:unifying Feature and Metric learning for patch-based MatchingUnderstanding Neural Networks Through Deep visualizationCo

From Cold War to deep learning: An Illustrated History of machine translationSelected from vas3k.comIlya PestovEnglish Translator: Vasily ZubarevChinese Translator: Panda The dream of high quality machine translation has been around for many years and many scientists have contributed their time and effort to this dream. From early rule-based machine translation to today's widely used neural machine

Gradient Based Learning 1 Depth Feedforward network (Deep Feedforward Network), also known as feedforward neural network or multilayer perceptron (multilayer PERCEPTRON,MLP), Feedforward means that information in this neural network is only a single direction of forward propagation without feedback mechanism. 2 Rectifier Linear unit (rectified linear Unit,relu), has some beautiful properties, more suitable

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning.learning with gradient descent algorithm (learning with gradient descent)1. TargetWe want an algorithm that allows us to find weights and biases so that the output y (x) of the network can fit all the training input x.2. Price functions (cost function)Define a cost funct

information.The query results cannot be mapped to the Pojo property of the Pojo object using Resulttype, and the Resulttype or Resultmap is chosen based on the need for the result set query traversal.CollectionFunction: Maps The associated query information to a list collection.Occasion: In order to facilitate the wiping of the associated information can be used collection to map the associated information to the list collection, such as: Query the user Rights Range module and the menu under th

MongoDB Tour (4) In-depth study of Postedon ------------------------------------------------------ tips: SKU: StockkeepingUnit, minimum inventory unit. Db. categories. find ({_ id: {$ in: product [category_ids]}); db. products. renameCollection (store_produc MongoDB Tour (4) In-depth study of Posted on ------------------------------------------------------ tips: SKU: Stock keeping Unit, minimum inventory Unit. Db. categories. find ({_ id: {$ in: product ['category _ id']}); db. products. renameC

Js deep learning notes (1)Js is a simple introduction. new Foo (): 1. the prototype of the object directs to the prototype attribute of the Foo constructor. The advantage is that if the object does not exist when accessing the property of the object, the prototype attribute value of Foo will be searched based on the prototype chain; 2. true indicates that the property belongs to the prototype chain of the o