hinton neural networks

This series of blogs is summarized according to Geoffrey Hinton course neural Network for machine learning. The course website is:Https://www.coursera.org/course/neuralnets1. Some examples The most applicable field example of the tasks best solved by learning machine learning-Recognizing patterns: pattern recognition–objects in real scenes object recognition–facial identities or facial expressions face dete

The third lecture of Professor Geoffrey Hinton's Neuron Networks for machine learning mainly introduces linear/logical neural networks and backpropagation, and the following is a tidy note.Learning the weights of a linear neuronThis section introduces the learning algorithms for linear neural

ImageNet classification with deep convolutional neural Networks reading notes(after deciding to read a paper each time, the notes are recorded on the blog.) ）This article, published in NIPS2012, was Hinton and his students, in response to doubts about deep learning, used deep learning for imagenet, the largest database of image recognition, and eventually achieve

1. Neural networksRoughly speaking, a neural network is a set of connected input/output units. Each connection is associated with a weight. In the learning phase, by adjusting these weights, we can predict the correct class labels of input tuples for learning. Due to the connection between units, neural network learning is also called connectionist learning ).

. AI technology in game programming . (Serialization) Introduce Neural Networks in common languages(Neural Networks in plain English) Because we don't have a good understanding of the brain, we often try to use the latest technology as a model to explain it. In my childhood, we all believed that the brain wa

This series of articles is the study notes of "machine learning", by Prof Andrew Ng, Stanford University. This article is the notes of week 5, neural Networks learning. This article contains some topic on cost Function and backpropagation algorithm.Cost Function and BackPropagationNeural networks is one of the most powerful learning algorithms, we have today. In

Oxford University and a researcher at Google DeepMind.Vggnet explores the relationship between the depth of convolutional neural networks and their performance, by repeatedly stacking 3*3 's small convolution cores and 2*2 's largest pooled layer,Vggnet successfully constructed a convolutional neural network with deep 16~19 layer. Vggnet compared to the previous

The authors of this paper take two typical imbalances as examples, this paper systematically studies and compares various methods to solve the problem of category imbalance in CNN, and makes experiments on three common data sets Minist, CIFAR-10 and Imagenet, and obtains the comprehensive result, which is rich in reference and instructive significance. Thesis Link: https://arxiv.org/abs/1710.05381 Absrtact: In this paper, we systematically study the effect of class imbalance in convolution

Order: This series is based on the neuralnetwork and deep learning book, and I have written my own insights. I wrote this series for the first time. What's wrong! Next, we will introduce neural networks so that you can understand what neural networks are. For better learning, we will be guided by identification numbers

The original book: "AI Technology in Game programming" Excerpt from: http://blog.csdn.net/starxu85/article/details/3143533 Original: http://blog.csdn.net/zzwu/article/category/243067 . (one of the serials) introduce neural networks in normal language(neural Networks in Plain 中文版) Because we don't have a go

**************************************Note: This blog series is for bloggers to learn the "machine learning" course notes from Professor Andrew Ng of Stanford University. Bloggers deeply learned the course, do not summarize is easy to forget, according to the course plus their own to do not understand the problem of the addition of this series of blogs. This blog series includes linear regression, logistic regression, neural network, machine learning