coursera gcp

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

Anomaly Detection and Recommender SystemsThis week's programming job is divided into two parts: anomaly detection and referral system.Anomaly Detection: The essence is to use the Gaussian distribution of the sample to the special value to estimate

The origin of Neural network Considering a nonlinear classification, when the number of features is very small, the logical regression can be completed, but when the feature number becomes larger, the higher order term will be exponential growth,

Building your recurrent neural network-step by step Welcome to Course 5 ' s-A-assignment! In this assignment, you'll implement your The recurrent neural network in NumPy. Recurrent neural Networks (RNN) are very effective for Natural Language

The recent Wunda study of the five-door sequence model finally came out, I took some time, just completed the course, I have to say, Ng's fifth Class I am still very satisfied with the video and the work is very good, the job content is also very

1. How to become a better learner metaphor and analogy helps to learn without jealousy genius 1. How to become a better learner the biggest gift for your brain is exercising more. we used to think that the brain was basically stereotyped after

Mini-project Description-rock-paper-scissors-lizard-spockRock-paper-scissors is a hand game this is played by the people. The players count to three in unison and simultaneously "throw" one of the three hand signals this correspond to rock, paper O

Mini-project description-"Guess the number" gameOne of the simplest two-player games is "Guess the number". The first player thinks of a secret number in some known range while the second player attempts to guess the number. After each guess, the

1. Open the URL https://www.coursera.org Register, then search for the course you want to study, no certificate is required for free2. If the video has been buffered or displays a black screen, you need to modify the

This section mainly reviews some simple knowledge about linear algebra.Matrix and vector Matrix Number of $ m \ times N $ A _ {IJ} (I = ,..., m; j = 1, 2 ,..., n) $ the number table of $ M $ row $ N $ column, which is called the matrix of $ M $ row $

Mainly for the sixth week Content machine learning application recommendations and system design.What to do nextWhen training good one model, predicting unknown data discovery, how to improve it? Get more examples of training Try to

In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains

This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is

I've been talking about why machines can learn, and starting with this lesson are some basic machine learning algorithms, i.e. how machines learn.This lesson is about linear regression, starting with the minimization of Ein, introducing the Hat

Mainly for the ninth week content: Anomaly detection, recommendation system(i) Anomaly detection (DENSITY estimation) kernel density estimation ( Kernel density estimation X (1) , X (2) ,.., x (m) If the data set is normal, we want to know