20 machine learning questions that will make you think.

(Cool questions. Not the regular, introductory stuff that you find everywhere.)

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1. Why is it important to introduce non-linearities in a neural network?

2. What are the differences between a multi-class classification problem and a multi-label classification problem?

3. Why does the use of Dropout work as a regularizer?

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4. Why you shouldn't use a softmax output activation function in a multi-label classification problem when using a one-hot-encoded target?

5. Does the use of Dropout in your model slow down or speed up the training process? Why?

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6. In a Linear or Logistic Regression problem, do all Gradient Descent algorithms lead to the same model, provided you let them run long enough?

7. Explain the difference between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.

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8. What are the advantages of Convolution Neural Networks (CNN) over a fully connected network for image classification?

9. What are the advantages of Recurrent Neural Networks (RNN) over a fully connected network when working with text data?

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10. How do you deal with the vanishing gradient problem?

11. How do you deal with the exploding gradient problem?

12. Are feature engineering and feature extraction still needed when applying Deep Learning?

13. How does Batch Normalization help?

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14. The universal approximation theorem shows that any function can be approximated as closely as needed using a single nonlinearity. Then why do we use more?

15. What are some of the limitations of Deep Learning?

16. Is there any value in weight initialization?

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17. The training loss of your model is high and almost equal to the validation loss. What does it mean? What can you do?

18. Assuming you are using Batch Gradient Descent, what advantage would you get from shuffling your training dataset?

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19. Compare the following evaluation protocols: a hold-out validation set, K-fold cross-validation, and iterated K-fold validation. When would you use each one?

20. What are the main differences between Adam and the Gradient Descent optimization algorithms?

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I'll be posting an in-depth answer for each one of these questions over the next following days.

Stay tuned and help me spread more machine learning content to more people in the community!
Feel free to post answers for those questions that you know. Give them a try!

You’ll be forced to think about them just by trying to collect your thoughts to put them here.

(There’s nothing as effective to learn than interacting with others.)

More from Santiago

You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through


You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

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Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

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Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

👇

More from All

MASTER THREAD on Short Strangles.

Curated the best tweets from the best traders who are exceptional at managing strangles.

• Positional Strangles
• Intraday Strangles
• Position Sizing
• How to do Adjustments
• Plenty of Examples
• When to avoid
• Exit Criteria

How to sell Strangles in weekly expiry as explained by boss himself. @Mitesh_Engr

• When to sell
• How to do Adjustments
• Exit


Beautiful explanation on positional option selling by @Mitesh_Engr
Sir on how to sell low premium strangles yourself without paying anyone. This is a free mini course in


1st Live example of managing a strangle by Mitesh Sir. @Mitesh_Engr

• Sold Strangles 20% cap used
• Added 20% cap more when in profit
• Booked profitable leg and rolled up
• Kept rolling up profitable leg
• Booked loss in calls
• Sold only


2nd example by @Mitesh_Engr Sir on converting a directional trade into strangles. Option Sellers can use this for consistent profit.

• Identified a reversal and sold puts

• Puts decayed a lot

• When achieved 2% profit through puts then sold

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