We aren't doing this near enough.
Some of the things I've learned in more than 20 years in the tech industry.
You need to hear these.
🧵👇
We aren't doing this near enough.
No small improvement is too small.
Just aim for something new every day, and you'll be surprised at the end.
Be the person that pulls everyone out of the rabbit holes.
Great results will get you farther than processes, but good processes can help you achieve good results.
It's funny how everything you share finds a way to reward you back.
We all make mistakes. Move on from them and focus on what's coming.
Ask away!
(There are, however, stupid people with fragile egos that get bothered when others ask. Ignore them.)
Embrace change.
People fantasize about perfection, but perfectionism rarely wins.
Shipping more often will give you better odds than gilding the lily.
What you know today will be outdated tomorrow.
Make a plan to keep up and follow it... or you'll get behind.
(And it looks horrible in your resume.)
More from Santiago
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
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%)
👇