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Tags: deviation chinese data cts You multitasking performance GPO ESCLearning Goals
Understand what multi-task learning and transfer learning is
Recognize bias, variance and data-mismatch by looking in the performances of your algorithm on train/dev/test sets
"Chinese Translation"Learning GoalsLearn what multi-tasking
decision on one input is Muti-task learning, and the other is learning a classifier for each category. When the sample data of each class is small, it is wiser to adopt muti-task learning, so that the characteristics of the image can be learned better, and when the sample data of each class is large, the multi-classification model can be used, so the accuracy of
networks and overfitting:
The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ):
It has a low computing cost.
The following is a "big" Neural Network (which has many parameters and is easy to overfit ):
It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method.
References:
Machine Learning
continuously updating theta.
Map Reduce and Data Parallelism:
Many learning algorithms can be expressed as computing sums of functions over the training set.
We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel.
Week 11:Photo OCR:
Pipeline:
Text detection
Character segmentation
Ch
What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was sa
friends, but also hope to get the high people of God's criticism! Preface [Machine Learning] The Coursera Note series was compiled with notes from the course I studied at the Coursera learning (Andrew ng teacher). The content covers linear regression, logistic regre
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 functions
J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption
Starting from 0, 0 (or any other valu
is close to the global minimum. In fact, you can dynamically adjust the learning rate α= constant 1/(number of iterations + constant 2), so that as the iteration, α gradually reduced, in favor of the final convergence to the global minimum value. However, because "constant 1" and "Constant 2" is not OK, so often set α is fixed.How do you judge the convergence of the model as the iteration progresses? Every 1000 or 5,000 samples, the J value of these
Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully con
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
This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA
Download
Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no un
This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural network, the back is not necessarily seen.Of cour
regression.
The root number can also be selected based on the actual situation.Regular Equation
In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $.
For example, four groups of property price forecasts:
Least Squares
$ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regular equations Gradient Descent:
Desired stride $ \ Alpha $;
Multiple iterations are requ
Overview
Cost Function and BackPropagation
Cost Function
BackPropagation algorithm
BackPropagation Intuition
Back propagation in practice
Implementation Note:unrolling Parameters
Gradient Check
Random initialization
Put It together
Application of Neural Networks
Autonomous Driving
Review
Log
2/10/2017:all the videos; Puzzled about Backprogation
2/11/2017:reviewed backpropaga
would the Vectorize this code to run without all for loops? Check all the Apply.
A: v = A * x;
B: v = Ax;
C: V =x ' * A;
D: v = SUM (A * x);
Answer: A. v = a * x;
v = ax:undefined function or variable ' Ax '.
4.Say you has a vectors v and Wwith 7 elements (i.e., they has dimensions 7x1). Consider the following code:
z = 0;
For i = 1:7
Z = z + V (i) * W (i)
End
Which of the following vectorizations correctly compute Z? Check all the Apply.
IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford Andrew Ms Ng's tutorials in Coursera
-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T
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