Take some code you want to understand and ask ChatGPT to explain it.
I've found explanations are very detailed. This is much quicker than trying to figure out convoluted code.
2 of 16
11 ways ChatGPT saves me hours of work every day, and why you'll never outcompete those who use AI effectively.
A list for those who write code:
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Take some code you want to understand and ask ChatGPT to explain it.
I've found explanations are very detailed. This is much quicker than trying to figure out convoluted code.
2 of 16
Ask ChatGPT to improve existing code by describing what you want to accomplish.
It will give you instructions about how to do it, including the modified code.
3 of 16
This is great when refactoring code written by non-native Python developers who used a different naming convention.
Notice how ChatGPT not only gives you the updated code; it also explains the reason for the changes.
4 of 16
Very helpful when reviewing and refactoring code written by non-native Python developers.
ChatGPT knows the "Pythonic" way, and it will give you suggestions to improve your code and make it much more readable.
5 of 16
This is one of my favorite tricks: Ask ChatGPT to simplify complex code.
The result will be a much more compact version of the original code.
Notice the explanation and how it tells us this is simpler but not the most efficient.
6 of 16
This has become one of my favorite ChatGPT abilities: Ask it to help you test a function, and it will write test cases for you.
This example focuses on the quick_sort function from the previous example.
7 of 16
ChatGPT told me its Quick Sort implementation wasn't the most efficient, so I asked for an alternative implementation.
This is great when you want to explore different ways to accomplish the same thing.
I've learned a ton from this!
8 of 16
Anytime you want to port some code from one language to another, ask ChatGPT to help you.
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This is another one of my favorite tricks.
Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job.
It even includes usage examples as part of the documentation!
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If you are having trouble finding a bug in your code, ask ChatGPT for help.
It took ChatGPT seconds to find the bug in the attached example. I don't know about you, but it would have taken me much longer than that.
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Probably the way I use it the most is to kick off the structure of anything new I want to write. GitHub Copilot does a great job at this as well.
An example where this is very useful is when interacting with a RESTful API.
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I have 2+ decades of programming experience. I like to think I know what I'm doing.
I don't trust people's code (especially mine,) and I surely don't trust ChatGPT's output.
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ChatGPT is flawed. I find it makes mistakes when dealing with code, but that's why I'm here: to supervise it.
Together we form a more perfect Union. (Sorry, couldn't help it)
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The story is not about ChatGPT taking programmers' jobs. It's not about a missing import here or a subtle mistake there.
The story is how, overnight, AI gives programmers a 100x boost.
Ignore this at your own peril.
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I'll only ask questions.
It'll blow your mind.
Follow me @svpino to ensure you don't miss it.
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More from Santiago
10 machine learning YouTube videos.
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
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%)
👇
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1/12
RT-PCR corona (test) scam
Symptomatic people are tested for one and only one respiratory virus. This means that other acute respiratory infections are reclassified as
2/12
It is tested exquisitely with a hypersensitive non-specific RT-PCR test / Ct >35 (>30 is nonsense, >35 is madness), without considering Ct and clinical context. This means that more acute respiratory infections are reclassified as
3/12
The Drosten RT-PCR test is fabricated in a way that each country and laboratory perform it differently at too high Ct and that the high rate of false positives increases massively due to cross-reaction with other (corona) viruses in the "flu
4/12
Even asymptomatic, previously called healthy, people are tested (en masse) in this way, although there is no epidemiologically relevant asymptomatic transmission. This means that even healthy people are declared as COVID
5/12
Deaths within 28 days after a positive RT-PCR test from whatever cause are designated as deaths WITH COVID. This means that other causes of death are reclassified as
RT-PCR corona (test) scam
Symptomatic people are tested for one and only one respiratory virus. This means that other acute respiratory infections are reclassified as
4/10
— Dr. Thomas Binder, MD (@Thomas_Binder) October 22, 2020
...indication, first of all that testing for a (single) respiratory virus is done outside of surveillance systems or need for specific therapy, but even so the lack of consideration of Ct, symptoms and clinical findings when interpreting its result. https://t.co/gHH6kwRdZG
2/12
It is tested exquisitely with a hypersensitive non-specific RT-PCR test / Ct >35 (>30 is nonsense, >35 is madness), without considering Ct and clinical context. This means that more acute respiratory infections are reclassified as
6/10
— Dr. Thomas Binder, MD (@Thomas_Binder) October 22, 2020
The neither validated nor standardised hypersensitive RT-PCR test / Ct 35-45 for SARS-CoV-2 is abused to mislabel (also) other diseases, especially influenza, as COVID-19.https://t.co/AkFIfTCTkS
3/12
The Drosten RT-PCR test is fabricated in a way that each country and laboratory perform it differently at too high Ct and that the high rate of false positives increases massively due to cross-reaction with other (corona) viruses in the "flu
External peer review of the RTPCR test to detect SARS-CoV-2 reveals 10 major scientific flaws at the molecular and methodological level: consequences for false positive results.https://t.co/mbNY8bdw1p pic.twitter.com/OQBD4grMth
— Dr. Thomas Binder, MD (@Thomas_Binder) November 29, 2020
4/12
Even asymptomatic, previously called healthy, people are tested (en masse) in this way, although there is no epidemiologically relevant asymptomatic transmission. This means that even healthy people are declared as COVID
Thread web\u2b06\ufe0f\u2b07\ufe0f
— Dr. Thomas Binder, MD (@Thomas_Binder) December 16, 2020
The fabrication of the "asymptomatic (super) spreader" is the coronation of the total nons(ci)ense in the belief system of #CoronasWitnesses.
Asymptomatic transmission 0.7%; 95% CI 0%-4.9% - could well be 0%!https://t.co/VeZTzxXfvT
5/12
Deaths within 28 days after a positive RT-PCR test from whatever cause are designated as deaths WITH COVID. This means that other causes of death are reclassified as
8/8
— Dr. Thomas Binder, MD (@Thomas_Binder) March 24, 2020
By the way, who the f*** created this obviously (almost) worldwide definition of #CoronaDeath?
This is not only medical malpractice, this is utterly insane!https://t.co/FFsTx4L2mw