coursera italian

Early in the morning, I started various checks at the hotel and experienced ibis breakfast again. The only pity is that I didn't have time to buy stamps. I just went to Paris to play with it, and Sunday was an early morning train. Even though the

Wine data comes from the UCI database and records the chemical composition of wine 13 of different varieties in the same region of Italy, so as to achieve automatic wine Classification through scientific methods. The data of this classification has

How to let the lack of new ideas in the photo of new tricks, below and everyone to share a course of the crow, teach you to use PS to create Zen-aesthetic flowers. 1. Comparison diagram and analysis of ideas Analysis: The

First, how to learn a large-scale data set?In the case of a large training sample set, we can take a small sample to learn the model, such as m=1000, and then draw the corresponding learning curve. If the model is found to be of high deviation

Welcome and Introductionoverviewreadinglog 9/9 videos and quiz completed; 10/29 Review; Note1.1 Welcome 1) What are machine learning? Machine learning are the science of getting compters to learn, without being

 II. Linear Regression with one Variable (Week 1)-Model representationIn the case of previous predictions of house prices, let's say that our training set of regression questions (Training set) looks like this:We use the following notation to

This week's programming work is mainly two-part content.1.k-means Clustering.2.PCA (Principle Component analys) principal component analysis.The main method is to compress the image by clustering the image, and then it is found that PCA can compress

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general

Neural networks:learning Last week's course learned the neural network forward propagation algorithm, this week's course mainly lies in the neural network reverse renewal process. 1.1 Cost function Let's recall the value function of logistic

Deep Learning & art:neural Style Transfer Welcome to the second assignment of this week. In this assignment, you'll learn about neural Style Transfer. This algorithm is created by Gatys et al. (https://arxiv.org/abs/1508.06576). in this assignment,

5.1 Section cost FunctionThe cost function of a neural network.Review some of the concepts in neural networks:L the total number of layers of the neural network.Number of units of the SL-L layer (excluding deviation units).Category 2 Classification

You can access the Google drive containing all of the current and in-progress lecture slides for this course through the L Ink below. Lecture Slides You could find it helpful to either bookmark this page or download the slides for easy

You can access the Google drive containing all of the current and in-progress lecture slides for this course through the L Ink below. Lecture Slides You could find it helpful to either bookmark this page or download the slides for easy

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 learning algorithm is learning is not how the model behaves on

Extremely light of a semester finally passed, summer vacation intends to learn the big step down this machine learning techniques.The first lesson is the introduction of SVM, although I have learned it before, but I heard a feeling is very rewarding.

This section is about overfitting, listening to the understanding of overfitting more profound than before.First introduced the overfitting, the consequence is that Ein is very small, and eout is very large. Then the causes of overfitting are

This section is about regularization, in the optimization of the use of regularization, in class when the teacher a word, not too much explanation. After listening to this class,To understand the difference between a good university and a pheasant

-Feature ScalingWhen we are faced with multidimensional feature problems, we need to ensure that the multidimensional features have similar scales, which will help the gradient descent algorithm to converge faster.Take the housing price forecast

-Cost functionFor the training set and our assumptions, we will consider how to determine the coefficients in the assumptions.What we are going to do now is to choose the right parameters, and the selection of parameters directly affects the

Vi. Logistic Regression (Week 3)-ClassificationIn the classification problem, what we try to predict is whether the result belongs to a certain class (for example, correct or error). Examples of classification problems include determining whether an