SQL Joins

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Joins are used to combine rows from two or more tables based on related columns between them. Joins allow you to retrieve data from multiple tables simultaneously, enabling you to create complex queries that fetch data from different sources. There are different types of joins in SQL, including: INNER JOIN Returns only the rows that have matching values in both tables based on the specified join condition. It discards non-matching rows from both tables. Example:           create table t1(x int); insert into t1 values(1); insert into t1 values(1); insert into t1 values(0); create table t2(y int); insert into t2 values(0); insert into t2 values(1); insert into t2 values(1);           select * from t1 inner join t2 on t1.x = t2.y Output: 2. LEFT JOIN (or) LEFT OUTER JOIN Returns all the rows from the left (or first) table and the matching rows from the right (or second) table. If there is no match, NULL values are
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Machine Learning Foundations - Deep Learning Summary - Quiz

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1) Which of the following statements are true? (Check all that apply)

a) Linear classifiers are never useful, because they cannot represent XOR.
b) Linear classifiers are useful, because, with enough data, they can represent anything.
c) Having good non-linear features can allow us to learn very accurate linear classifiers.
d) none of the above

2) A simple linear classifier can represent which of the following functions? (Check all that apply)
Hint: If you are stuck, see https://www.coursera.org/learn/ml-foundations/module/nqC1t/discussions/AAIUurrtEeWGphLhfbPAyQ

a) x1 OR x2 OR NOT x3
b) x1 AND x2 AND NOT x3
c) x1 OR (x2 AND NOT x3)
d) none of the above

3) Which of the the following neural networks can represent the following function? Select all that apply.
(x1 AND x2) OR (NOT x1 AND NOT x2)
Hint: If you are stuck, see https://www.coursera.org/learn/ml-foundations/module/nqC1t/discussions/AAIUurrtEeWGphLhfbPAyQ

d

4) Which of the following statements is true? (Check all that apply)

a) Features in computer vision act like local detectors.
b) Deep learning has had impact in computer vision, because it’s used to combine all the different hand-created features that already exist.
c) By learning non-linear features, neural networks have allowed us to automatically learn detectors for computer vision.
d) none of the above

5) If you have lots of images of different types of plankton labeled with their species name, and lots of computational resources, what would you expect to perform better predictions:

a) a deep neural network trained on this data.
b) a simple classifier trained on this data, using deep features as input, which were trained using ImageNet data.

6) If you have a few images of different types of plankton labeled with their species name, what would you expect to perform better predictions:

a) a deep neural network trained on this data.
b) a simple classifier trained on this data, using deep features as input, which were trained using ImageNet data.

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