coursera robotics

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

Mainly for the week content: large-scale machine learning, cases, summary(i) Random gradient descent methodIf there is a large-scale training set, the normal batch gradient descent method needs to calculate the sum of squares of errors across the

This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout

-Cost functionFor linear regression models, the cost function we define is the sum of squares of all model errors. In theory, we can also follow the definition of a logistic regression model, but the problem is that when we bring it into the cost

-Polynomial regressionSince linear regression does not apply to all data, sometimes we need to use curves to fit our data, for example, with two-times polynomial:Or three-time polynomial:Usually we need to look at the data before deciding what model

 Iv. Linear Regression with multiple Variables (Week 2)-Multiple featuresBefore we introduced the Univariate/single feature regression model, we now add more variables to the house price forecast model, which is more features, such as the number

-Unsupervised learningIn supervised learning, whether it is a regression problem or a classification problem, we use the data to have a clear label or the corresponding prediction results.In unsupervised learning, our existing data have no

-Supervised learningFor supervised learning let's look at an example, which is an example of a house price forecast. The horizontal axis of the figure shows the floor space, and the ordinate indicates the price of the house transaction. Each fork in

1. Open FileUse Handle=open (Filename,mode) to open the file. This function will return a handle (which should be translated as "handle") to manipulate the file, and the parameter filename is a string. The parameter mode is optional, ' R ' stands

Autonomous Driving-car Detection Welcome to your Week 3 programming assignment. You'll learn about object detection using the very powerful YOLO model. Many of the "ideas in" notebook are described in the two YOLO et al., Papers:redmon (2016 2640)

Face recognition Face verification vs. face recognition One-Shot LearningFor example, you want to set up a face recognition for the company, but in general, you will not have too many photos of employees, if you follow the previous practice to

Character level language Model-dinosaurus Land Welcome to Dinosaurus island! Million years ago, dinosaurs existed, and in this assignment they is back. You is in charge of a special task. Leading biology researchers was creating new breeds of

ResnetsThe identity blockThe convolutional block (you can use this type of block when the input and output dimensions don ' t match up. The conv2d layer in the shortcut path was used to resize the input xx to a different dimension, so that the

one of the pits in this emojify is that AVG initialization must be (50,) if you use (word_to_vec_map["a"]). Shape just can't live. emojify! Welcome to the second assignment of Week 2. You is going to the use of Word vector representations to

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