teched learning

Bayesian Introduction Bayesian learning Method characteristic Bayes rule maximum hypothesis example basic probability formula table Machine learning learning speed is not fast enough, but hope to learn more down-to-earth. After all, although it is it but more biased in mathematics, so to learn the rigorous and thorough, can be better applied to the right scene.

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 processing APIs, these APIs integrate Apple's so-called Core ML framework. The Core of Core ML is to accelerate AI tasks on the iPhone, iPad

[Machine learning algorithm-python implementation] matrix denoising and normalization, python Machine Learning1. The background project is required. We plan to use python to implement matrix denoising and normalization. The numpy mathematical library does not find ideal functions. Therefore, I wrote a de-noise and normalization algorithm in the standard library, which is a little inefficient, but still usable, you can use it if needed. (1) denoising a

Series of articles Directory Index :《 Mistakes and methods in English Learning" 　　 Preface Greg Thomson, a famous linguistics, said: "The principle of foreign language learning is so complicated that no one can clearly understand it. However, the process of mastering a language is so simple that it does not need to be clear ." It is very complicated to discuss the principles of language

Microsoft Azure cloud service introduces the machine learning module. Users only need to upload data and use some algorithm interfaces and R or other language interfaces provided by the machine learning module, you can use Microsoft Azure's powerful cloud computing capabilities to implement your machine learning tasks. This document introduces the machine

Today, Google's robot Alphago won the second game against Li Shishi, and I also entered the stage of the probability map model learning module. Machine learning fascinating and daunting.--Preface1. Learning based on PGMThe topological structure of Ann Networks is often similar. The same set of models are trained in different samples, and different results can be

convolutional Neural Network is the first multi-layered neural network structure which has been successfully trained, and has strong fault tolerance, self-learning and parallel processing ability.First, the basic principle1.CNN algorithm Ideasconvolutional neural network can be regarded as a special case of Feedforward network, which simplifies and improves Feedforward network mainly in network structure, in theory, the inverse propagation algorithm c

Gradient Descent Practical Tips II Learning rate Gradient descent in Practice II--learning ratesThe learning rate in the gradient descent algorithm (learning rates) is difficult to determine, and here are some practical tips to look for. First look at how to determine your gradient descent algorithm is working properly

Connect Because we want to learn the expression of features, we need to know more about features or hierarchical features. So before we talk about deep learning, we need to explain the features again (haha, we actually see such a good explanation of the features, but it is a pity that we don't put them here, so we are stuck here ). Iv. Features Features are the raw material of the machine learning

Python Learning 1, python Learning 1. Introduction to python Introduction to Python on various websites Python is powerful... and fast; Plays well with others; Runs everywhere; Is friendly easy to learn; Is Open. 2. python version differences Short version: Python 2.x is legacy, Python 3.x is the present and future of the language Python 2.7 is a compatible version, but Python 2.7 wocould be supported u

Python Machine Learning Theory and Practice (4) Logistic regression and python Learning Theory From this section, I started to go to "regular" machine learning. The reason is "regular" because it starts to establish a value function (cost function) and then optimizes the value function to obtain the weight, then test and verify. This entire process is an essentia

, and X is characteristic.As described above, let's try to define these two ways to solve the problem:discriminant Learning Algorithm (discriminative learning algorithm): Direct Learning P (y|x) or method of direct mapping from input to outputGenerate learning Algorithm (generative

/* Copyright NOTICE: You can reprint, reprint, please be sure to indicate the original source of the article and author information. Author: Zhang Junlin Timestamp:2014-10-3 This paper mainly summarizes the application methods and techniques of deep learning in natural language processing in the last two years, and the relevant PPT content please refer to this link, which lists the main outlines. Brief introduction to outline and depth of study