machine learning framework

actions.The derived class relationships of two concrete classes and corresponding qevent are as follows: Qsignaltransition Qstatemachine::signalevent Qeventtransition Qstatemachine::wrappedevent After entering the state, the state machine registers the corresponding transition of the active states: For the signal type, connect the signal to a private slot function that generates an internal

Today began to study Stanford University CS229 course, do not want to completely copy the handout, hope to add their own understanding, beginners, inevitably error, welcome correct. The first lesson introduces the knowledge framework for machine learning, CS229, as long as the inductive reasoning approach in machine

likelihood solution. For finite data sets, the posteriori mean of parameter μ is always between the transcendental average and the maximum likelihood estimate of μ.SummarizeAs we can see, the posterior distribution becomes an increasingly steep peak shape as the observational data increases. This is shown by the variance of the beta distributions, when a and b approach infinity, the variance of the beta distribution tends to be nearly 0. At a macro level, when we observe more data, the uncertai

mixture Models SummaryProgramming Assignment 1Programming Assignment 2========================================================================================================########### #chapter5: Mixed membership Modeling via latent Dirichlet allocation#############========================================================================================================Introduction to latent Dirichlet allocation LDA introductionBayesian inference via Gibbs sampling Bayesian inference based on Gi

TensorFlow v0.11.0 RC1 Released, TensorFlow is Google's second-generation machine learning system, according to Google, in some benchmarks, tensorflow performance than the first generation of distbelief faster than twice times. Extended support for TensorFlow depth learning, any computation that can be expressed using computational flow graphs can be used with T

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit that, as a beginner, may not be in the early st

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

analyzes the theoretical basis of evolutionary optimization for most evolutionary algorithms, which often depend on the insufficiency of heuristic algorithms. By drawing on the multi-layered framework of deep learning, Professor Chen Yu has developed hierarchical Bayesian analysis and online variable decibel Dean inference method in the 4th chapter. In the 5th chapter, Dr. Li Yu and Professor Zhou Zhihua d

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu