machine learning nanodegree

After tossing the crawler and some interesting content, I recently in the R language for simple machine learning knowledge, the main reference is "machine learning-Practical Case Analysis" this book.This book is a rare, purely r language-based machine

converge or even diverge. .One thing worth noting:As we approach the local minimum, the guide values will automatically become smaller, so the gradient drop will automatically take a smaller amplitude, which is the practice of gradient descent. So there's actually no need to reduce the alpha in addition, we need a fixed (constant) learning rate α. 4. Gradient Descent linear regression (Gradient descent for Linear Regression) This is the method of us

: Random initialization Loop until convergence { Each State transfer count in the sample is used to update and R Use the estimated parameters to update V (using the value iteration method of the previous section) According to the updated V to re-draw } In step (b) We are going to do a value update, which is also a loop iteration, in the previous section we solved v by initializing v t

1. Nearest Neighbor Component analysis (NCA) algorithmAbove content reproduced from: http://blog.csdn.net/chlele0105/article/details/130064432. Metric LearningIn machine learning, the main purpose of dimensionality reduction of high dimensional data is to find a suitable low-dimensional space, in which the learning can be better than the original space performanc

I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating.1. Test and Training error: Why lower training error was not always a good thing:esl figure 2.11. Test and training error as a function of model complexity.2. Under and overfitting: PRML figure 1.4. Plots of polynomials has various orders M, shown as red curves, fitted

Learning Guide for machine learning beginners (experience sharing)2013-09-21 14:47I computer research two, the professional direction of natural language processing, individuals interested in machine learning, so began to learn. So, this guy is a rookie ... It is because of

After 2 months of knowledge of machine learning. I've found that machine learning has a variety of directions. Page sort. Speech recognition, image recognition, recommender system, etc. Algorithms are also varied. After seeing the other books, I found that except for the K-mean clustering. Bayesian, neural network, onl

Project applicability analysis of main machine learning algorithmsSome time ago Alphago with the Li Shishi of the war and related deep study of the news brush over and over the circle of friends. Just this thing, but also in the depth of machine learning to further expand, and the breadth of

you to someone.Daughter: How old are you?Mother: 26.Daughter: Long handsome not handsome?Mother: Very handsome.Daughter: Is the income high?Mother: Not very high, medium condition.Daughter: Is it a civil servant?Mother: Yes, I work in the Inland Revenue Department.Daughter: Well, I'll meet you.Use decision trees to represent:As a code farmer often will constantly knock if, else if, else, in fact, has been used in the decision tree thinking. Just have you ever thought, there are so many conditio

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job four q13-20 MATLAB implementation. The previous code was implemented through C + +, but found that C + + implementation of the code is too cumbersome, the job also to change the parameter values frequently, so choose to use

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job four q13-20 MATLAB implementation.Once the code is implemented through C + +. However, it is too cumbersome to discover that C + + implements this code. This job also need to change the number of participants frequently, so

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learni