Discover coursera neural networks, include the articles, news, trends, analysis and practical advice about coursera neural networks on alibabacloud.com

Over the past few days, I have read some peripheral materials around the paper a neural probability language model, such as Neural Networks and gradient descent algorithms. Then I have extended my understanding of linear algebra, probability theory, and derivation. In general, I learned a lot. Below are some notes.
I,Neural

is the number of nodes related to the classification, assuming that we are set to 10 classes, the output layer is 10 nodes, the corresponding expectations of the setting in the multilayer neural network has been introduced, each output node and the above hidden layer 100 nodes connected, total (100+1) *10=1010 link line, 1010 weights.As can be seen from the above, the core of convolutional neural

Instructor Ge yiming's "self-built neural network writing" e-book was launched in Baidu reading.
Home page:Http://t.cn/RPjZvzs.
Self-built neural networks are intended for smart device enthusiasts, computer science enthusiasts, geeks, programmers, AI enthusiasts, and IOT practitioners, it is the first and only Neural

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning. Catalogue
Perceptual device
S-type neurons
The architecture of the neural network
Using neural networks to recognize handwritten numbers
Towards Deep learn

This chapter is a total of two parts, this is the second part:14th-cyclic neural networks (recurrent neural Networks) (Part I) chapter 14th-Cyclic neural networks (recurrent neural

This article is from here, the content of this blog is Java Open source, distributed deep Learning Project deeplearning4j The introduction of learning documents.
Introduction:in general, neural networks are often used for unsupervised learning, classification, and regression. That is, neural networks can help grou

As a free from the vulgar Code of the farm, the Spring Festival holiday Idle, decided to do some interesting things to kill time, happened to see this paper: A neural style of convolutional neural networks, translated convolutional neural network style migration. This is not the "Twilight Girl" Kristin's research direc

Learning Goals
Understand the convolution operation
Understand the pooling operation
Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...)
Build a convolutional neural network for Image Multi-Class classification
"Chinese Translation"Learning GoalsUnderstanding convolution OperationsUnderstanding pooling Operationsremember vocabulary used in co

neural network:step by StepWelcome to your Week 4 assignment (Part 1 of 2)! You are previously trained a 2-layer neural Network (with a single hidden layer). This week, you'll build a deep neural network with the as many layers as you want!
In this notebook, you'll implement all the functions required to build a deep

Two types of classification: binary Multi-ClassThe following are two types of classification problems (one is binary classification, one is Multi-Class classification)If it is a binary classification classification problem, then the output layer has only one node (1 output unit, SL =1), hθ (x) is a real number,k=1 (K represents the node number in the output layer).Multi-Class Classification (with K categories): hθ (x) is a k-dimensional vector, SL =k, generally k>=3 (because if there are two cl

network prediction
Total number of layers $L $-neural network (including input and output layers)
$\theta^{(L)}$-the weight matrix of the $l$ layer to the $l+1$ layer
$s _l$-the number of neurons in the $l$ layer, note that $i$ counts from 1, and the weights of bias neurons are not counted in the regular term.
The number of neurons in the _{l+1}$-layer of the $s $l+1$
Reference documents[1] Andrew Ng

)/∂ (θ (1) JK) is tested for gradients. After the partial derivative code does not have a problem, close the Gradient check section code.6. Use gradient descent or other advanced algorithms to perform reverse propagation to find the θ values for minimizing j (θ).This paper describes the gradient descent algorithm in neural networks: starting from the random initial point, descending step by step, until the

Why use sequence models (sequence model)? There are two problems with the standard fully connected neural network (fully connected neural network) processing sequence: 1) The input and output layer lengths of the fully connected neural network are fixed, and the input and output of different sequences may have different lengths, Selecting the maximum length and f

nervous system, electrophysiological pulses and pulse neural networks compare to the analogue output of a computer, which determines the likelihood of topological and bio-neurological hypotheses.There is a major difference between the impulse neural network and the proven theory in practice. Pulsed neural

1000x1000x1000000=10^12 connection, that is, 10^12 weight parameters. However, the spatial connection of the image is local, just like the human being through a local feeling field to feel the external image, each neuron does not need to feel the global image, each neuron only feel the local image area, and then at higher levels, The overall information can be obtained by synthesizing the neurons with different local feelings . In this way, we can reduce the number of connections, that is, to r

Content
Overview
Word Recognition system LeNet-5
Simplified LeNet-5 System
The realization of convolutional neural network
Deep neural network has achieved unprecedented success in the fields of speech recognition, image recognition and so on. I have been exposed to neural networks many years

Previous 4ArticleThis is a fuzzy system, which is different from the traditional value logic. The theoretical basis is fuzzy mathematics, so some friends are confused. If you are interested, please refer to relevant books, I recommend the "fuzzy mathematics tutorial", the National Defense Industry Press, which is very comprehensive and cheap (I bought 7 yuan ). Introduction to Artificial Neural Networks
Ar

Source: Michael Nielsen's "Neural Network and Deep leraning"This section translator: Hit Scir master Xu Zixiang (Https://github.com/endyul)Disclaimer: We will not periodically serialize the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" hit SCIR "public number, reprint has obtained consent. "
Using

full implementation of multi-layered neural network recognition picture of the cat Original Coursera Course homepage, in the NetEase cloud classroom also has the curriculum resources but no programming practice. This program uses the functions completed in the last job, fully implementing a multilayer neural network, and training to identify whether there is a

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.