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This document was written by one of the major Java gods who wanted to learn. NET at level 15. I think, blog Park is the place where I grow and progress, as a Zhuang with the Internet to enjoy bi spirit of literary female youth, I should share it here to give more need to want to learn. NET children's shoes let them go to grow, let them less to learn some detours, write unreasonable place, welcome everyone criticize correct, or have better study suggestions and

supervised learning , which is often said to be classified, is trained to obtain an optimal model (a set of functions, the best of which is optimal under a certain evaluation criterion) through the training sample (known data and its corresponding output). Using this model to map all the input to the corresponding output, the output is simply judged to achieve the purpose of classification, it also has the ability to classify the unknown data. In peop

9. Common models or methods of deep learning 9.1 autoencoder automatic Encoder One of the simplest ways of deep learning is to use the features of artificial neural networks. Artificial Neural Networks (ANN) itself are hierarchical systems. If a neural network is given, let's assume that the output is the same as the input, and then train and adjust its parameters to get the weight in each layer. Naturally,

1. Google Cloud Machine learning Platform Introduction:The three elements of machine learning are data sources, computing resources, and models. Google has a strong support in these three areas: Google not only has a rich variety of data resources, but also has a strong computer group to provide data storage in the data computing capacity, at the same time, research and implementation of TensorFlow this mac

Why Study Reinforcement Learning Reinforcement Learning is one of the fields I ' m most excited about. Over the past few years amazing results like learning to play Atari Games from Raw Pixelsand Mastering the Game of Go have Gotten a lot of attention, but RL is also widely used in robotics, Image processing and Natural Language processing. Combining reinforcem

CatalogueOne: Introduction two:. NET technology System Three: Common Tools Summary Four: Learning Resources Summary Five: Book recommendation Six: The experience of reading technical Books VII: summaryOne: IntroductionBecause of the work adjustment, from PHP Development 0 Foundation to. NET development, there is not much free time to learn the system. Have to find a way to quickly grasp the current needs to meet the methods, and share with you:It's no

1. What is machine learningMachine learning is the conversion of unordered data into useful information.The main task of machine learning is to classify and another task is to return.Supervised learning: It is called supervised learning because such algorithms must know what to predict, that is, the categorical informa

SVM is a widely used classifier, the full name of support vector machines , that is, SVM, in the absence of learning, my understanding of this classifier Chinese character is support/vector machines, after learning, Only to know that the original name is the support vector/machine, I understand this classifier is: by the sparse nature of a series of support vectors to get a better classifier, this classifie

CI framework learning Notes (1)-Environment installation, basic terms and framework processes, ci learning notes. CI framework learning Notes (1)-Environment installation, basic terms, and framework processes. when ci learning notes first use the CI framework, they plan to write a series of notes for CI source code rea

Anyone who knows a little bit about supervised machine learning will know that we first train the training model, then test the model effect on the test set, and finally deploy the algorithm on the unknown data set. However, our goal is to hope that the algorithm has a good classification effect on the unknown data set (that is, the lowest generalization error), why the model with the least training error will also be effective in controlling the gene

descent, batch gradient processing uses all M example for parameter updating, and the random gradient descent only uses 1 example to update the parameters, while the mini gradient descent uses B (1Repeat {For i = 1, 11, 21, ..., 991 {$\theta_j=\theta_j-\alpha\frac{1}{10}\sum\limits_{k=i}^{i+9} (H_\theta (x^{(k)})-y^{(k)}) x_j^{(k)}$}}Convergence of algorithmsBatch gradient processing can ensure that the algorithm converges to the minimum (if the selected le