machine learning and neural networks

number that it is not able to reflect the actual data situation. But if we take all the samples we have as a training sample, the opportunity is already a real sample set, so the fact that the overfitting is not there. Although neural networks are theoretically flawed, these flaws are no longer a problem by the increase in computational power and data. For the reasons above, the hot spots of

Before explaining the error back propagation algorithm, let's review the flow of the signal in the neural network. Please understand that when input vector \ (x\) input Perceptron, the first initialization weight vector \ (w\) is randomly composed, can also be understood as we arbitrarily set the initial value, and the input do dot product operation, and then the model through the weight update formula to calculate the new weight value , the updated w

. Neural Networks (13.2%) and boosting (~9%) performed well. The higher the data dimension, the more random forests are stronger than the adaboost, but the overall is less than svm[2]. The greater the amount of data, the stronger the neural network.Nearest neighbor (nearest neighbor) A typical example is KNN, which is the idea--for the point to be judged, find th

high, it will increase the burden and storage space of training, dimensionality reduction is the redundancy that wants to remove the feature, and the feature is represented by less dimension. The most fundamental of the dimensionality reduction algorithm is PCA, and many of the algorithms are based on PCA.Common algorithms for machine learningThere are many algorithms and models involved in machine

. Overwhelmed by machine Learning:is there a ML101 book: This is a problem on the StackOverflow. A range of machine learning recommended books are available. The first answer that Jeff Moser provides is useful, including links to course videos and lectures. Related theories, books, papers, courses, blogs: [Book]Y

1.computer Vision CV is an important direction of deep learning, CV generally includes: image recognition, target detection, neural style conversion Traditional neural network problems exist: the image of the input dimension is larger, as shown, this causes the weight of the W dimension is larger, then he occupies a larger amount of memory, calculate W calculati

This note describes the third week of convolutional neural networks: Target detection (1) Basic object detection algorithmThe main contents are:1. Target positioning2. Feature Point detection3. Target detectionTarget positioningUse the algorithm to determine whether the image is the target object, if you want to also mark the picture of its position and use the border marked outAmong the problems we have st

Core ML machine learning, coreml Machine Learning At the WWDC 2017 Developer Conference, Apple announced a series of new machine learning APIs for developers, including visual APIs for facial recognition and natural language proce

After figuring out the fundamentals of convolutional Neural Networks (CNN), in this post we will discuss the algorithm implementation techniques based on Theano. We will also use mnist handwritten numeral recognition as an example to create a convolutional neural network (CNN) to train the network so that the recognition error reaches within 1%.We first need to r

collection of individuals), to select, Exchange, and mutate the individual according to the evaluation Value (fitness), So as to get new groups. Genetic algorithms are suitable for very complex and difficult environments, for example, with a lot of noise and irrelevant data, things are constantly updated, problem targets cannot be clearly and precisely defined, and the value of current behavior can be determined through a lengthy execution process. As with