identity Archives - Jacob is studying on programming

04Jan 2019

Machine Learning by Andrew Ng week 1 ( Summary )

by jacob

key words: Gradient Descent,Supervised Learning ,Unsupervised Learning, Cost Function,

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.We can derive this structure by clustering the data based on relationships among the variables in the data.With unsupervised learning there is no feedback based on the prediction results.Example:Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).https://www.coursera.org/learn/machine-learning/supplement/NKVJ0/supervised-learning

Supervised Learning

In supervised learning, we are given a data set , having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems.

(a) Regression 예시- Given a picture of a person, we have to predict their age on the basis of the given picture

(b) Classification 예시 – Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

https://www.coursera.org/learn/machine-learning/lecture/olRZo/unsupervised-learning

Unsupervised Learning

Supervised Learning, we were told explicitly what is the so-called right answer.

In Unsupervised Learning, we’re given data that doesn’t have any labels or that all has the same label or really no labels. this is called a clustering algorithm. One example where clustering is used is in Google News. Google News has done is look for tens of thousands of news stories and automatically cluster them together. So, the news stories that are all about the same topic get displayed together.

https://www.coursera.org/learn/machine-learning/supplement/1O0Bk/unsupervised-learning

Unsupervised Learning

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results.

Example:

Clustering: google new, sales segments

Non-clustering: The “Cocktail Party Algorithm”

https://www.coursera.org/learn/machine-learning/supplement/cRa2m/model-representation

Model Representation

data set 과 training set은 같은 뜻이며 처음 원천적으로 주어지는 data이다.

m 은 data set에서의 example갯수, 즉 data set row갯수이다.

x와 y 위에 지수처럼 있는 ( i )는 data set에서의 index. 즉 몇번째 example인지를 알려준다.

https://www.coursera.org/learn/machine-learning/supplement/nhzyF/cost-function

Cost Function

We can measure the accuracy of our hypothesis function by using a cost function.

https://www.coursera.org/learn/machine-learning/supplement/u3qF5/cost-function-intuition-i

Cost Function – Intuition I

https://www.coursera.org/learn/machine-learning/supplement/9SEeJ/cost-function-intuition-ii

Cost Function – Intuition II

는 예상기울기 예상biased 값을 이용해서 나온 예상값이다. Ɵ0 는 biased value 이고 Ɵ1는 기울기이다.

는 cost를 구하는 cost function

https://www.coursera.org/learn/machine-learning/supplement/2GnUg/gradient-descent

Gradient Descent

So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That’s where gradient descent comes in.

:= 이 기호는 우측계산 값을 오른 쪽에 대입한다는 의미이다. 수학에서 a = b는 a와 b는 같다는 claim이다.

https://www.coursera.org/learn/machine-learning/supplement/QKEdR/gradient-descent-intuition

Gradient Descent Intuition

이전의 예시에서는 slope, biased value 두개를 고려한 예시들이었다. 여기서는 이해를 위해 slope 하나만 고려한다.

https://www.coursera.org/learn/machine-learning/supplement/U90DX/gradient-descent-for-linear-regression

Gradient Descent For Linear Regression

위의 설명에서 시그마안의 내용이 이해가 잘 가지 않았으나 실제로 쎄타에대해 편미분을 해보면 식이 나오는 것을 알수 있었다. 참조 사향 ) https://youtu.be/vsWrXfO3wWw?t=893

https://www.coursera.org/learn/machine-learning/supplement/Q6mSN/matrices-and-vectors

Matrices and Vectors

https://www.coursera.org/learn/machine-learning/supplement/FenyC/addition-and-scalar-multiplication

Addition and Scalar Multiplication

https://www.coursera.org/learn/machine-learning/lecture/aQDta/matrix-vector-multiplication

https://www.coursera.org/learn/machine-learning/supplement/cgVgM/matrix-vector-multiplication

Matrix-Vector Multiplication

https://www.coursera.org/learn/machine-learning/lecture/dpF1j/matrix-matrix-multiplication

https://www.coursera.org/learn/machine-learning/supplement/l0myT/matrix-matrix-multiplication

Matrix-Matrix Multiplication

https://www.coursera.org/learn/machine-learning/lecture/W1LNU/matrix-multiplication-properties

https://www.coursera.org/learn/machine-learning/supplement/Xl0xT/matrix-multiplication-properties

Matrix Multiplication Properties

https://www.coursera.org/learn/machine-learning/lecture/FuSWY/inverse-and-transpose

https://www.coursera.org/learn/machine-learning/supplement/EcNto/inverse-and-transpose

Inverse and Transpose

04Jan 2019

Machine Learning by Andrew Ng week 1 ( Summary )

by jacob

key words: Gradient Descent,Supervised Learning ,Unsupervised Learning, Cost Function,

Supervised Learning

In supervised learning, we are given a data set , having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems.

(a) Regression 예시- Given a picture of a person, we have to predict their age on the basis of the given picture

(b) Classification 예시 – Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

https://www.coursera.org/learn/machine-learning/lecture/olRZo/unsupervised-learning

Unsupervised Learning

Supervised Learning, we were told explicitly what is the so-called right answer.

https://www.coursera.org/learn/machine-learning/supplement/1O0Bk/unsupervised-learning

Unsupervised Learning

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results.

Example:

Clustering: google new, sales segments

Non-clustering: The “Cocktail Party Algorithm”

https://www.coursera.org/learn/machine-learning/supplement/cRa2m/model-representation

Model Representation

data set 과 training set은 같은 뜻이며 처음 원천적으로 주어지는 data이다.

m 은 data set에서의 example갯수, 즉 data set row갯수이다.

x와 y 위에 지수처럼 있는 ( i )는 data set에서의 index. 즉 몇번째 example인지를 알려준다.

https://www.coursera.org/learn/machine-learning/supplement/nhzyF/cost-function

Cost Function

We can measure the accuracy of our hypothesis function by using a cost function.

https://www.coursera.org/learn/machine-learning/supplement/u3qF5/cost-function-intuition-i

Cost Function – Intuition I

https://www.coursera.org/learn/machine-learning/supplement/9SEeJ/cost-function-intuition-ii

Cost Function – Intuition II

는 예상기울기 예상biased 값을 이용해서 나온 예상값이다. Ɵ0 는 biased value 이고 Ɵ1는 기울기이다.

는 cost를 구하는 cost function

https://www.coursera.org/learn/machine-learning/supplement/2GnUg/gradient-descent

Gradient Descent

:= 이 기호는 우측계산 값을 오른 쪽에 대입한다는 의미이다. 수학에서 a = b는 a와 b는 같다는 claim이다.

https://www.coursera.org/learn/machine-learning/supplement/QKEdR/gradient-descent-intuition

Gradient Descent Intuition

이전의 예시에서는 slope, biased value 두개를 고려한 예시들이었다. 여기서는 이해를 위해 slope 하나만 고려한다.

https://www.coursera.org/learn/machine-learning/supplement/U90DX/gradient-descent-for-linear-regression

Gradient Descent For Linear Regression

https://www.coursera.org/learn/machine-learning/supplement/Q6mSN/matrices-and-vectors

Matrices and Vectors

https://www.coursera.org/learn/machine-learning/supplement/FenyC/addition-and-scalar-multiplication

Addition and Scalar Multiplication

https://www.coursera.org/learn/machine-learning/lecture/aQDta/matrix-vector-multiplication

https://www.coursera.org/learn/machine-learning/supplement/cgVgM/matrix-vector-multiplication

Matrix-Vector Multiplication

https://www.coursera.org/learn/machine-learning/lecture/dpF1j/matrix-matrix-multiplication

https://www.coursera.org/learn/machine-learning/supplement/l0myT/matrix-matrix-multiplication

Matrix-Matrix Multiplication

https://www.coursera.org/learn/machine-learning/lecture/W1LNU/matrix-multiplication-properties

https://www.coursera.org/learn/machine-learning/supplement/Xl0xT/matrix-multiplication-properties

Matrix Multiplication Properties

https://www.coursera.org/learn/machine-learning/lecture/FuSWY/inverse-and-transpose

https://www.coursera.org/learn/machine-learning/supplement/EcNto/inverse-and-transpose

Inverse and Transpose

26May 2017

Google Sign-In for Android | Google Developers

by jacob ⋅ Link

Google Sign-In for Android | Google Developers

26May 2017

Google Sign-In for Android | Google Developers

by jacob ⋅ Link

Google Sign-In for Android | Google Developers

16Jun 2016

OpenID versus OAuth from the user’s perspective – cakebaker

by jacob ⋅ Link

OpenID versus OAuth from the user’s perspective – cakebaker

original source : http://cakebaker.42dh.com/2008/04/01/openid-versus-oauth-from-the-users-perspective/

In this article I want to show the differences between OpenID and its younger cousin OAuth by providing for each a typical user scenario.

First the scenario for OpenID:

User wants to access his account on example.com
example.com (the “Relying Party” in OpenID lingo) asks the user for his OpenID
User enters his OpenID
example.com redirects the user to his OpenID provider
User authenticates himself to the OpenID provider
OpenID provider redirects the user back to example.com
example.com allows the user to access his account

And now the scenario for OAuth:

User is on example.com and wants to import his contacts from mycontacts.com
example.com (the “Consumer” in OAuth lingo) redirects the user to mycontacts.com (the “Service Provider”)
User authenticates himself to mycontacts.com (which can happen by using OpenID)
mycontacts.com asks the user whether he wants to authorize example.com to access his contacts
User makes his choice
mycontacts.com redirects the user back to example.com
example.com retrieves the contacts from mycontacts.com
example.com informs the user that the import was successful

From those scenarios we can see that OpenID is about authentication (i.e. I can identify myself with an url) whereas OAuth is about authorization (i.e. I can grant permission to access my data on some website to another website, without providing this website the authentication information for the original website).

I hope this helps to keep apart those two standards 🙂

16Jun 2016

OpenID versus OAuth from the user’s perspective – cakebaker

by jacob ⋅ Link

OpenID versus OAuth from the user’s perspective – cakebaker

original source : http://cakebaker.42dh.com/2008/04/01/openid-versus-oauth-from-the-users-perspective/

In this article I want to show the differences between OpenID and its younger cousin OAuth by providing for each a typical user scenario.

First the scenario for OpenID:

User wants to access his account on example.com
example.com (the “Relying Party” in OpenID lingo) asks the user for his OpenID
User enters his OpenID
example.com redirects the user to his OpenID provider
User authenticates himself to the OpenID provider
OpenID provider redirects the user back to example.com
example.com allows the user to access his account

And now the scenario for OAuth:

User is on example.com and wants to import his contacts from mycontacts.com
example.com (the “Consumer” in OAuth lingo) redirects the user to mycontacts.com (the “Service Provider”)
User authenticates himself to mycontacts.com (which can happen by using OpenID)
mycontacts.com asks the user whether he wants to authorize example.com to access his contacts
User makes his choice
mycontacts.com redirects the user back to example.com
example.com retrieves the contacts from mycontacts.com
example.com informs the user that the import was successful

I hope this helps to keep apart those two standards 🙂

25May 2016

Google Sign In gapi undefined

by jacob ⋅ Leave a Comment ⋅ Link

Google Sign In gapi undefined

original source : http://stackoverflow.com/questions/25665865/google-sign-in-gapi-undefined

Your code is calling gapi.client.plus.people.get method before loading the google api library https://plus.google.com/js/client:plusone.js. Hence you are getting gapi is not defined error.

Approach to work-

Why its not working?

We are calling https://plus.google.com/js/client:plusone.js asynchronously(non blocking) to improve the performance. With Async javascript file loading, you are not able to call the gapi method on body load.

    <script type="text/javascript">
          (function() {
            var po = document.createElement('script');
            po.type = 'text/javascript'; po.async = true;
            po.src = 'https://plus.google.com/js/client:plusone.js';
            var s = document.getElementsByTagName('script')[0];
            s.parentNode.insertBefore(po, s);
    </script>

To make the api call you first have to know javascript file is successfully loaded.

For this you have to call method using callback. https://apis.google.com/js/client:plusone.js?onload=makeAPICall

Write an api request & execution it in the callback method to get data.

Check below example for this-

    <html>
    <head></head>
    <body>
    <span id="signinButton">
      <span
        class="g-signin"
        data-callback="signinCallback" 
        data-clientid="YOUR CLIENT ID.apps.googleusercontent.com"
        data-cookiepolicy="single_host_origin"
        data-scope="https://www.googleapis.com/auth/plus.login">
      </span>
    </span>
    <script type="text/javascript">      
      (function() {
         var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true;
         po.src = 'https://apis.google.com/js/client:plusone.js?onload=signinCallback';
         var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s);
      })();
      function signinCallback(authResult) {
            if (authResult['status']['signed_in']) {
                    document.getElementById('signinButton').setAttribute('style', 'display: none');
                        makeAPICall();
            } else {
                console.log('Sign-in state: ' + authResult['error']);
            }
      }
            function makeAPICall(){
            gapi.client.load('plus', 'v1', function() {
              var request = gapi.client.plus.people.get({
                'userId': 'me'
              });
              request.execute(function (resp){
                console.log(resp);
                if(resp.id){
                  console.log('ID: ' + resp.id);
                }
                if(resp.displayName){
                  console.log('Display Name: ' + resp.displayName);
                }
                if(resp.image && resp.image.url){
                  console.log('Image URL: ' + resp.image.url);
                }
                if(resp.url){
                  console.log('Profile URL: ' + resp.url);
                }
              });
           });
      }
    </script>
    </body>
    </html>

Conclusion: Calling javascript API before loading the asynchronously client library.

To avoid "Daily Limit for Unauthenticated Use Exceeded. Continued use requires signup.". Call makeAPICall() method only when user is logged in not on every request.

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Tag Archives: Identity

Machine Learning by Andrew Ng week 1 ( Summary )

Supervised Learning

Unsupervised Learning

Unsupervised Learning

Model Representation

Cost Function

Cost Function – Intuition I

Cost Function – Intuition II

Gradient Descent

Gradient Descent Intuition

Gradient Descent For Linear Regression

Matrices and Vectors

Addition and Scalar Multiplication

Matrix-Vector Multiplication

Matrix-Matrix Multiplication

Matrix Multiplication Properties

Inverse and Transpose

Machine Learning by Andrew Ng week 1 ( Summary )

Supervised Learning

Unsupervised Learning

Unsupervised Learning

Model Representation

Cost Function

Cost Function – Intuition I

Cost Function – Intuition II

Gradient Descent

Gradient Descent Intuition

Gradient Descent For Linear Regression

Matrices and Vectors

Addition and Scalar Multiplication

Matrix-Vector Multiplication

Matrix-Matrix Multiplication

Matrix Multiplication Properties

Inverse and Transpose

OpenID versus OAuth from the user’s perspective – cakebaker

OpenID versus OAuth from the user’s perspective – cakebaker

Post Navigation

Tag Archives: Identity

Machine Learning by Andrew Ng week 1 ( Summary )

Supervised Learning

Unsupervised Learning

Unsupervised Learning

Model Representation

Cost Function

Cost Function – Intuition I

Cost Function – Intuition II

Gradient Descent

Gradient Descent Intuition

Gradient Descent For Linear Regression

Matrices and Vectors

Addition and Scalar Multiplication

Matrix-Vector Multiplication

Matrix-Matrix Multiplication

Matrix Multiplication Properties

Inverse and Transpose

Share this:

Machine Learning by Andrew Ng week 1 ( Summary )

Supervised Learning

Unsupervised Learning

Unsupervised Learning

Model Representation

Cost Function

Cost Function – Intuition I

Cost Function – Intuition II

Gradient Descent

Gradient Descent Intuition

Gradient Descent For Linear Regression

Matrices and Vectors

Addition and Scalar Multiplication

Matrix-Vector Multiplication

Matrix-Matrix Multiplication

Matrix Multiplication Properties

Inverse and Transpose

Share this:

Google Sign-In for Android | Google Developers

Share this:

Google Sign-In for Android | Google Developers

Share this:

OpenID versus OAuth from the user’s perspective – cakebaker

Share this:

OpenID versus OAuth from the user’s perspective – cakebaker

Share this:

Google Sign In gapi undefined

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Post Navigation