BC GPT CHEAT LL
Saved by @Smatpix

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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 1. Explaining code

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.

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2. Improve existing code

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.

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3. Rewriting code using the correct style

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.

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4. Rewriting code using idiomatic constructs

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.

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5. Simplifying code

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.

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6. Writing test cases

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.

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7. Exploring alternatives

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!

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8. Translating code

Anytime you want to port some code from one language to another, ask ChatGPT to help you.

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9. Writing documentation

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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10. Tracking down bugs

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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11. Scaffolding

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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Something to keep in mind:

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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This is not about letting ChatGPT do my work. This is about using it to 10x my output.

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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Developers who shit on this are missing the point.

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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Next time, I'll show you how to build an end-to-end deep learning model using ChatGPT.

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

Santiago
Santiago
@svpino
I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."


This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.
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Santiago
Santiago
@svpino
I learned enough AI/ML in 12 months to change my life forever.

You can do the same, and it all starts with a killer process to learn new things.

You can steal my approach. Every detail on the latest issue of my newsletter: https://t.co/9gaenLByFb.

Make it work for you.
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@svpino
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.)

🧵👇

1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.

2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.

3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.

4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.
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Santiago
Santiago
@svpino
A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

• Class A: 90% accuracy
• Class B: 80% accuracy
• Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ↓

Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.

How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

• Class A: 95% accuracy
• Class B: 100% accuracy
• Class C: 71% accuracy

Do you see what just happened?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you 😉.
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Santiago
Santiago
@svpino
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
MACHINE LEARNING

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'Treasure Gods' - Sangha Nidhi' and 'Padma Nidhi', Tirumala, Tirupati.

------------------------------------

These are the two guards guarding the Wealth and Treasure of Lord Sri Venkateswara.

They are representatives of two treasures of Kubera, the God of Wealth.


One guard who is holding shangam (conch) on both the hands is known as Shanka Nidhi ('nidhi' means wealth).

On the other side, the guard who is holding Lotus flowers on both the hands is known as Padma nidhi.

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Switch from WhatsApp to Signal and enable disappearing messages. Most of us cannot stop using WhatsApp entirely but at least turn off back ups to Google Drive or iCloud. 2/n

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