Santiago Profile picture
17 Mar, 15 tweets, 4 min read
Here are some of the features that make Python 🐍 a freaking cool language.

🧵👇
1. You can slice and dice arrays very easily.
2. What's even better: negative indexing is really cool, and you can use it to refer to items from the last element in the list.
3. Unpacking lets you assign an iterable of values to different variables in a single statement.
4. f-strings let you format strings in a concise and readable way.
5. You can zip iterables together and create parallel iterators.
6. You can construct new sequences using comprehensions, an elegant way to make stuff happens in a single line.
7. You can use generator expressions to create an iterator that will generate elements on-demand.
8. Chaining comparison operators is a blast.
9. You can create new types dynamically in a very simple way.
10. Before I move to the rest of the list, you don't need to worry about curly braces or semicolons in Python.

In my book, that's a big win!
Here are even more cool features of the language:

▫️ Imaginary numbers
▫️ for-else loops
▫️ try-except-else expressions
▫️ Keyword arguments
▫️ Walrus operator
▫️ String interpolation
▫️ Tuples
▫️ Named tuples
▫️ Slice assignment
▫️ Naming slices
▫️ Sets and set operations
▫️ Lambda functions
▫️ The with statement
▫️ Decorators
▫️ Descriptors
▫️ Enumeration
And it's not only about features; Python is much more than that:

▫️ Easy to learn
▫️ Easy to write
▫️ Free and Open Source
▫️ Portable
▫️ Mature community
▫️ Large sponsors
▫️ Versatile

Python lovers, what else am I forgetting here? Non-Python users, what are you waiting for?
Python has whatever you are looking for. There’s a library for that.

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More from @svpino

18 Mar
I can't shut up about neural networks.

What questions do you have?
They aren't necessarily opposite concepts.

Fully connected refer to networks composed of layers where every node is connected to every node of the next layer.

Deep networks refer to networks with many layers. They could be fully connected or not.

Especially with deep learning, where you have many layers full of nodes, it's hard to understand the "thinking" of a network because you'll have to reverse-engineer million of float values and try to make sense of them.

Hard to do.

Read 7 tweets
18 Mar
The ability to reuse the knowledge of one model and adapt it to solve a different problem is one of the most consequential breakthroughs in machine learning.

Grab your ☕️ and let's talk about this.

🧵👇
A deep learning model is like a Lego set, with many pieces connected, forming a long structure.

These pieces are layers, and each layer has a responsibility.
Although we don't know exactly the role of every layer, we know that the closer they get to the output, the more specific they get.

The best way to understand what I mean is through an example: a model that will process car images.
Read 13 tweets
16 Mar
5 Python 🐍 package managers that I'm not using anymore:

▫️ conda
▫️ virtualenv
▫️ venv
▫️ pipenv
▫️ poetry

🤷‍♂️

Instead, for several weeks now, I've been using development containers in Visual Studio Code.

Life-changing. Give 'em a try.
Here is a thread I wrote a few weeks back when I started using them:
An important note: here I’m referring to the “virtual environment” capabilities of these tools. I still need to pip modules down.

But I’ve been isolating environments with the containers instead.
Read 5 tweets
15 Mar
You aren't doing yourself any favors if you aren't throwing away your validation data regularly.

It's painful, I know, but you are looking for trouble if you don't do it.

Let's talk about what happens with your data and your model.

Grab the ☕️, and let's do this thing. 🧵👇
Every machine learning tutorial teaches you about splitting your dataset.

They either go with train/test or train/validation/test. Nomenclature doesn't matter here. You just need to understand how each one of these is used.

Here is a thread about this:

Let's think of a neural network and focus on the train set for a second.

We use this to train our model. The data on this set is the one the network uses to adjust the weights.

And, of course, the model will get really good at solving this set.
Read 8 tweets
15 Mar
My recommendation to learn machine learning:

▫️ Machine Learning Crash Course (Google)
▫️ Machine Learning (Coursera)

Take them in order. They are both free. They are both amazing.

(Before you embark on this journey, make sure you feel comfortable writing Python 🐍.)
Not really. Nothing has changed with the fundamentals. The course is as relevant today as it was back in 2010.
Experience will help you make a few sensible choices that you can later test.
Read 8 tweets
14 Mar
A good way to understand how shit works is by breaking it down as much as you can.

Here is some code showing Dropout working on an array. And this is a thread explaining how it works.

☕️🧵👇
First, the code.

I want you to notice that Dropout does a couple of things:

▫️ It zeroes-out a percentage of the units.
▫️ It scales the remaining units to account for the missing values.

The second one wasn't obvious to me.
Remember those kids from school that sat together and copied from each other during exams?

They aced every test but were hardly brilliant, remember?

Eventually, the teacher had to set them apart. That was the only way to force them to learn.
Read 9 tweets

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