coursera robotics

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

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

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