194

mongo db $250 promotion code.

 

You’re a valued Realm Cloud customer, we’re reaching out to notify of a change affecting your support experience.

 

As you’re probably aware, Realm was acquired by MongoDB in April 2019. Since then, we’ve been working on understanding the various customer interactions and are now making some changes, including the move to a new portal experience and to the MongoDB Atlas Developer Support plan.

 

With this change, you can expect:

• Improved response to your high priority cases
• 8-hour initial response time SLA on S1 - Blocker issues
• 24/7, 365 access to the Support portal where our experienced engineers will offer more Realm-specific assistance and guidance
• End-to-end assistance and advice from our dedicated team, as well as access to knowledge base articles created by MongoDB and Realm engineers
• Increased visibility and access to your cases at any time via one unified platform

What’s next: 

• We’re deprecating the current Realm Support portal on September 7, 2020
• Sign up for Atlas Developer support for $49/month by creating a free Atlas account and to replace your current Realm Cloud plan (this means your monthly Realm Cloud plan will remain, but you will only be paying $49 a month).
• Please make sure to name your org with the naming convention "Realm Cloud - ” e.g. “Realm Cloud - Test” 
• If you sign up for the new portal experience between now and June 30, you’ll receive 250 free credits to Atlas, MongoDB’s Database-as-a-Service by applying this MDBSUPPORT-TIRUXR

 

If you have any questions or concerns, please contact us by replying to this email.

 

Thank you for being a valued MongoDB customer!

 

Julia Prause

Global Director, Customer Success

 

DetailsDate AddedActive DatesTotal AmountUsedAvailable

PROMO MDBSUPPORT-TIRUXR

04/23/20

04/23/20 (EXPIRES  12/31/99)

$250.00

$0.00

$250.00

find the menu titled APPLY CREDIT in billing.

 

I don't know of any way to change this behavior.


nstimer 안되어 dispatch_async(dispatch_get_main_queue(), ^{ 로 대
//    [NSTimer scheduledTimerWithTimeInterval:2.0
//                                                            target:self
//                                                          selector:@selector(timeoutHandler:)
//                                                          userInfo:nil
//                                                           repeats:NO];



import numpy as np
from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
x, resid, rank, s = np.linalg.lstsq(X, y)
print(x)



Boston house prices dataset
---------------------------
**Data Set Characteristics:**  
    :Number of Instances: 506 
    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.
    :Attribute Information (in order):
        1. CRIM     per capita crime rate by town
        2. ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        3. INDUS    proportion of non-retail business acres per town
        4. CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        5. NOX      nitric oxides concentration (parts per 10 million)
        6. RM       average number of rooms per dwelling
        7. AGE      proportion of owner-occupied units built prior to 1940
        8. DIS      weighted distances to five Boston employment centres
        9. RAD      index of accessibility to radial highways
       10. TAX      full-value property-tax rate per $10,000
       11. PTRATIO  pupil-teacher ratio by town
       12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
       13. LSTAT    % lower status of the population
       14. MEDV     Median value of owner-occupied homes in $1000's


[-9.28965170e-02  4.87149552e-02 -4.05997958e-03  2.85399882e+00
 -2.86843637e+00  5.92814778e+00 -7.26933458e-03 -9.68514157e-01
  1.71151128e-01 -9.39621540e-03 -3.92190926e-01  1.49056102e-02
 -4.16304471e-01]

 

UIImagePickerController

The preview is handled for you by the UIImagePickerController, and the overlay is always visible until the controller is dismissed.

import numpy as np
from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
x, resid, rank, s = np.linalg.lstsq(X, y)
print(x)

1. 범죄율에 반비례한다.
2. 비소매사업지역 면적 비율에 비례한다.
3. 일산화 질소 농도에 반비례 한다.
4. 주택당 방 수에 비혜한다.
5. 중 하위 계층 비율에 반비례 한다.
6. 인구중 흑인 비율에 비례한다.
7. 학생/교사 비율에 반비례한다.
8. 25,000 평방피트를 초과 거주지역 비율에 반비례 한다.
9. 인구 찰스강의 경계에 위치한 경우에 증가한다.
10. 1940년 이전에 건축된 비율에 반비례 한다.
11. 방사형 고속도로까지의 거리에 반비례 한다.
12. 직업센터 거리에 비례한다.
13. 재산세율에 반비례 한다.

남자배우 = 여자배우 + (남자-여자)