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Original handout of Stanford Machine Learning Course
This resource is the original handout of the Stanford machine learning course, which is AndrewNg said that a total of 20 PDF files cover some important models, algorithms, and

wide range of user personalization services, such as marketing strategies for consumers
The key to machine learning
(pattern) There is a potential pattern or rule that can be learned
(Definition) is not easy to implement programmatically
(data) has information about a pattern
The actual definition of machine learningM

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundati

A probe into machine learning1. What is machine learningLearning refers to the skill that a person refines in the course of observing things, rather than learning, machine learning refers to the ability of a computer to gain some

increase or reduce the number of example (change 100 to 1000 or 10, etc.), reduce or increase the learning rate.elearning (Online learning)The previous algorithm has a fixed training set to train the model, when the model is well trained to classify and return the future example. Online learning is different, it updates the model parameters for each new example,

Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. deciding what to try next (
Determine what to do next
)
I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts:
1) Deciding what to try next (decide what to do next)
2) Evaluating a hypothesis (Evaluation hypothesis)
3) Model selection and training/validation/test sets (Model selection and training/verific

Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. Learning with large datasets (
Big Data Learning
)
The importance of data volume has been mentioned in the previous lecture on machine learning design. Remember this sentence:

findF1scoreThe algorithm with the largest value. 5. Data for Machine Learning (
Machine Learning data
)
In machine learning, many methods can be used to predict the problem. Generally, when the data size increases, the accura

is that only the input paradigm is provided for this network, and it automatically identifies its potential class rules from those examples. When the study is complete and tested, it can also be applied to new cases.
A typical example of unsupervised learning is clustering. The purpose of clustering is to bring together things that are similar, and we do not care what this class is. Therefore, a clustering algorithm usually needs to know how to c

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has MATLAB changed to Python to be more fit to the

before, but you need to define T (Y) here:In addition, make:(t (y)) I represents the first element of the vector T (y), such as: (t (1)) 1=1 (T (1)) 2=01{.} is an indicator function, 1{true} = 1, 1{false} = 0(T (y)) i = 1{y = i}Thus, we can introduce the multivariate distribution of the exponential distribution family form:1.2 The goal is to predict the expectation of T (y), because T (y) is a vector, so the resulting output will also be a desired vector, where each element is:Corresponds to th

be trained and predicted immediately, which is called Online learning. each of the previously learned models can do online learning, but given the real-time nature, not every model can be updated in a short time and the next prediction, and the perceptron algorithm is well suited to do online learning:The parameter Update method is: if hθ (x) = y is accurate, the parameter is not updated otherwise, θ:=θ+ y

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine

First, Introduction1. Concept :
The field of study that gives computers the ability to learn without being explicitly programmed. --an older, informal definition by Arthur Samuel (for tasks that cannot be programmed directly to enable the machine to learn)
"A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves wit

Hope to learn the gospel of the Children of Learning machine, the world's largest AI company Google launched a "machine learning Crash Course", not only the whole Chinese, but also free to listen to OH.
The course is 15 hours, th

represent the right side of the inequality and Delta to represent ε.
So we have:
We have previously studied the probability of occurrence of bad events. Now let's look at the probability of occurrence of optimistic events:
P [| ein (G)-eout (G) |
Use Ω (n, H, Delta) instead of ε to get the desired good event definition: | eout-Ein |
Ω is positively related to N, Delta, and h or VC.
We ignore the Ω parameter first, so there are: | eout-Ein |
In most cases, eout is larger than EIN, because w

+ 1 parameter: x0 -- x256. We hope to use machine learning to determine the values of all these parameters. However, with so many parameters, machine learning may take a lot of time to complete, and the effect is not necessarily good. We can see that some pixels are not needed, so we should extract some features from

It should be this time last year, I started to get into the knowledge of machine learning, then the introductory book is "Introduction to data mining." Swallowed read the various well-known classifiers: Decision Tree, naive Bayesian, SVM, neural network, random forest and so on; In addition, more serious review of statistics, learning the linear regression, but a

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