Santiago Profile picture
24 Apr, 4 tweets, 3 min read
A book that will significantly help with your Python 🐍 skills:

• "Effective Python. 90 specific ways to write better Python." from Brett Slatkin @haxor.

amzn.to/3tM655V

Make sure you buy the second edition.

↓ 1/3 Image
The book contains 90 short lessons. Attached is the entire list.

Each lesson will take you a few minutes, which is extremely powerful:

• Finish lunch 🍱
• Grab the book
• Read one lesson
• Find a place in your code to apply it
• Put back the book on the shelf

↓ 2/3 ImageImageImageImage
Do this consistently, and you will significantly improve your Python skills.

Post a summary here on Twitter of what you learned for even better results.

This book works best for those who already write Python. It's not what you need to start from scratch.

3/3
If you are looking to learn Python from scratch, then take a look at this thread:

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More from @svpino

26 Apr
20 quotes that I use as inspiration to change my life.

Print them out. Keep them close. Read them frequently. Reflect on them and the wisdom behind every word.

Let's start:

Humans are allergic to change. They love to say, "We've always done it this way." I try to fight that. That's why I have a clock on my wall that runs counterclockwise.

— Grace Hopper Image
With engineering, I view this year's failure as next year's opportunity to try it again.

Failures are not something to be avoided. You want to have them happen as quickly as you can so you can make progress rapidly.

— Gordon Moore Image
Read 21 tweets
23 Apr
I've been teaching people how to start with machine learning for more than a year now.

This is a thread with what I've learned and some advice if you are looking to start.

↓ 1/14
People enjoy the process of getting ready to start something new.

Like the first time they go to the gym: they buy supplements, shorts, shoes, and a new headband.

Unfortunately, this is not enough.

↓ 2/14
Most people never stop preparing.

They keep collecting books, tutorials, the "best" videos and ask the same "how to start" questions.

The only thing they forget: taking a concrete step forward.

↓ 3/14
Read 14 tweets
22 Apr
A 13-tweet introduction to one of the most basic structures used in machine learning: a tensor.

Understanding how tensors work is fundamental. They aren't complex but working with them may get confusing if you don't understand all the pieces.

Let's solve that today.

↓ 1/13
Three primary attributes define a tensor:

• Rank: Number of axes.
• Shape: Number of dimensions per axis.
• Data type: Type of data contained in it.

↓ 2/13
The rank of a tensor refers to the tensor's number of axes.

Examples:

• Rank of a matrix is 2.
• Rank of a vector is 1.
• Rank of a scalar is 0.

↓ 3/13
Read 14 tweets
21 Apr
700 people have watched "How To Get Started With Machine Learning." 86 have rated it.

Let's celebrate!

• You can buy the course today for $7.
• $0 if you don't like it.
• Back to $15 tomorrow.

gum.co/kBjbC/only7

If you can't afford it, keep reading:

For every copy I sell today, I'll give away one for free.

To apply for the free copy, reply below with why you think this course will help you.

I'll prioritize the best stories I read.

If you want to support my content, like/retweet this thread, so more people see it.
So far, 8 copies sold, and 8 free copies shared.

Thanks for the continuous support! It helps tremendously!
Read 8 tweets
21 Apr
Creating a good machine learning model is really sexy. That's what's different and where everyone focuses all of their attention.

But machine learning is much more than that.

A thread with a few thoughts about the real job.

1/9
Machine learning engineers spend a lot of time designing and training new models, but this is just a small fraction of their job.

2/9
In reality, dealing with data and operationalizing models is much more time-consuming and sometimes even harder and more involved than creating the models in the first place.

3/9
Read 10 tweets
20 Apr
The backbone of my end-to-end machine learning setup:

• A 48-page Field Notes
• Python
• NumPy, Pandas, Matplotlib, OpenCV
• Scikit-Learn, XGBoost
• TensorFlow
• Google Colab, Jupyter, VSCode
• Docker, Flask
• AWS SageMaker
I personally don't use C/C++.

That doesn't mean it's not useful. I know plenty of people in the industry that rely on C/C++ to do their work.

It just means that I personally haven't needed it.

There are a lot of satellite tools that I have to use depending on the project. Kinesis, Airflow, SQS... the list is endless.

I just tried to list the core of what I need, and it rarely varies.

Read 5 tweets

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