In other words, the function should not use an auxiliary array to do the work.
11 short programming problems to stretch your imagination and make sure you are staying on your toes.
(Starting with the simple ones, they get more fun as you move towards the end.)
🧵👇
In other words, the function should not use an auxiliary array to do the work.
Only one of the numbers will appear twice.
There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.
If you want more content on software engineering, machine learning, and adjacent topics, give me a follow. I post threads like this every week. You can enjoy more of this content here: @svpino.
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.
👇
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
👇
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%)
👇
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
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
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
👇
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%)
👇
Here is a simple example of a machine learning model.
I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.
It's TensorFlow and Keras.
If you are starting out, this may be a good puzzle to solve.
The goal of this model is to learn to multiply one-digit
I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.
It's TensorFlow and Keras.
If you are starting out, this may be a good puzzle to solve.
The goal of this model is to learn to multiply one-digit
It is a good example of coding, what is the model?
— Freddy Rojas Cama (@freddyrojascama) February 1, 2021
12 machine learning YouTube videos.
On libraries, algorithms, tools, and theory.
↓
1. Jupyter Notebooks: https://t.co/HqE9yt8TkB
2. Pandas: https://t.co/aOLh0dcGF5
3. Matplotlib: https://t.co/tKADpmihkh
4. Seaborn: https://t.co/s8EUxh6x1f
5. Numpy: https://t.co/pJoc0Lfjwm
6. Decision Trees: https://t.co/tKtUpO1K3l
7. Neural Networks: https://t.co/bc7emyjc9q
8. Scikit-Learn: https://t.co/LrKG7cMxRq
9. TensorFlow: https://t.co/fhO6T9sblU
10. PyTorch: https://t.co/5w9mJxijdd
11. Essense of Linear Algebra: https://t.co/o3kOnxl90i
12. Essense of Calculus: https://t.co/rfo7v0cpR4
On libraries, algorithms, tools, and theory.
↓
1. Jupyter Notebooks: https://t.co/HqE9yt8TkB
2. Pandas: https://t.co/aOLh0dcGF5
3. Matplotlib: https://t.co/tKADpmihkh
4. Seaborn: https://t.co/s8EUxh6x1f
5. Numpy: https://t.co/pJoc0Lfjwm
6. Decision Trees: https://t.co/tKtUpO1K3l
7. Neural Networks: https://t.co/bc7emyjc9q
8. Scikit-Learn: https://t.co/LrKG7cMxRq
9. TensorFlow: https://t.co/fhO6T9sblU
10. PyTorch: https://t.co/5w9mJxijdd
11. Essense of Linear Algebra: https://t.co/o3kOnxl90i
12. Essense of Calculus: https://t.co/rfo7v0cpR4
More from Tech
The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the *hundreds of millions*. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.
This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/
A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/
https://t.co/uAORIJ5BYX 4/
https://t.co/yOGZ0pLfHG 5/
Flat Earth conference attendees explain how they have been brainwashed by YouTube and Infowarshttps://t.co/gqZwGXPOoc
— Raw Story (@RawStory) November 18, 2018
This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/
A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/
https://t.co/uAORIJ5BYX 4/
https://t.co/yOGZ0pLfHG 5/