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Programming Paradigm Lesson 1 Reading Notes: List several common programming languages (paradigms ): C Assembly C ++ Concurrency programming (Parallel programming) (just a paradigm, rather than a language, you can use C/C ++ to Implement Parallel

This section focuses on iOS drawings, gestures, protocols, blocks, mechanics animations (including gravity, collisions, adsorption, and so on) and the contents of the automatic layout.First, drawing, gesture(1) When invoking a custom UIView, you can

Reprint please indicate the sourcehttp://blog.csdn.net/pony_maggie/article/details/37370159Author: PonyThis lesson is about the concepts of view life cycle, network view, Image view, and scrolling view, as well as related demo demos. The first two

This section focuses on serial queues in multi-threading and scrolling view Uiscrollview. I. Multi-ThreadingThis section simply describes the multi-threaded serial queue, which is the sequential execution of the task by joining the thread queue.(1)

1.MVCModel: ModelsDescribe what the program is, such as database manipulation, and the card play is written on the model layer, through notification and KVO (subsequent articles will be introduced) two ways to communicate with the

Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised learning.

This topic (Machine Learning) including Single-parameter linear regression, multi-parameter linear regression, Octave tutorial, logistic regression, regularization, neural network, machine learning system design, SVM (Support Vector Machines support

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Motivation 1: Data Compression ( Motivation 1- Data Compression ) The so-called data compression is to reduce the dimension of high-dimensional

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. deciding what to try next ( Determine what to do next ) I have already introduced some machine learning methods. It is obviously not enough to know

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. The problem of overfitting ( Over-fitting ) Back to the linear regression problem that we first mentioned to predict the relationship between housing

Public Course address:Https://class.coursera.org/ml-003/class/index  INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price

1. Find the costfunction to measure the error 2. Fit the theta parameter to minimize the costfunction. Uses gradient descent, iterates n times, iteratively updates Theta, and reduces costfunction 3. Find the appropriate parameter theta for

This paper uses the regularization linear regression model pre-flow (water flowing out of dam) according to the water storage line (water level) of the reservoir, then the Debug Learning Algorithm and discusses the influence of deviation and

This article covers the following topics: Single-Variable linear regression Cost function Gradient Descent Single-Variable linear regressionLooking back at the next section, in the regression problem, we have given the input

16.1 problem formalization16.2 Content-based recommender system16.3 Collaborative Filtering16.4 Collaborative filtering algorithm16.5 vectorization: Low-rank matrix decomposition16.6 Implementation of work Details: Normalization of the mean value

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization,

19.1 Summary and acknowledgements Welcome to the last video on machine learning. We have been studying together for a long time. In the final video, I want to take a quick look at the main content of this course, and then briefly say a few words to

To draw a full stop to the first four sessions of the course, here are two of the models that were mentioned in the first four lectures by Andrew the Great God.The Perceptron Learning Algorithm Sensing machineModel:From the model, the Perceptron is

9. Clustering Content 9. Clustering 9.1 Supervised learning and unsupervised learning 9.2 K-means algorithm 9.3 Optimization Objective 9.4 Random Initialization 9.5 Choosing the number of Clusters 9.1 Supervised

The problem of regression is raised First, it needs to be clear that the fundamental purpose of the regression problem is prediction. For a problem, it is generally impossible to measure every situation (too much work), so we measure a set of