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15 Sep, 5 tweets, 2 min read
TensorFlow is currently the most popular end-to-end platform for Machine Learning.

Here you have a free 7-hour TensorFlow 2.0 course that's packed with everything you need to get started.

(A single hour per day can get you through this course in a week! Just one week!)

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The course is structured in 8 different modules that cover different aspects of Machine Learning and focus on how to apply TensorFlow 2.0 to solve different problems.

Here is the list of modules:

1⃣ Machine Learning Fundamentals
2⃣ Introduction to TensorFlow

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3⃣ Core Learning Algorithms
4⃣ Neural Networks with TensorFlow
5⃣ Deep Computer Vision - Convolutional Neural Networks
6⃣ Natural Language Processing with RNNs
7⃣ Reinforcement Learning with Q-Learning
8⃣ Conclusion and Next Steps

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In the video description, you'll find a set of Google Colab notebooks with all the code discussed in the modules.

This is an incredible resource that you get for free and will get you started in one of the most exciting open-source tools in the market today!

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Title: TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial.

YouTube Link:

The video was filmed by Tim from @TechWithTimm in conjunction with @freeCodeCamp and publish on their youtube channel. (Thank you so much for this!)

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More from @svpino

Santiago Profile picture
30 Sep
Greedy algorithms are super useful!

(And I mean in real life, not only for coding interviews!)

You are probably using them already, even if you don't know it, so let's explore how they work and —more importantly— when they don't.

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Here is the gist of a greedy algorithm: it builds a solution using the best option at every single step.

Here is the intuition (for when they work): "if we make the optimal option at every step, we will end up with the optimal solution overall."

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An example:

How do you make 73 cents using the least amount of coins? In the US, you will do the following:

▫️2 x 25 cents
▫️2 x 10 cents
▫️3 x 1 cent

At every step, I selected the larger denomination that fit.

This was a greedy solution. It's also the optimal solution.

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Read 9 tweets
Santiago Profile picture
29 Sep
If you want consistent results, you need a consistent process.

Here is every step I go through to tackle new Machine Learning problems.

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Step1⃣

▫️What exactly is the problem that you need to solve?
▫️Why do you need to solve this problem?

The answers should give you all the information you need to ensure a solid solution.

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Step2⃣

▫️What data do you have access to?
▫️What's the format of that data?
▫️How is that data going to be renewed/expanded?

Then you can focus on cleaning up the data and making it ready to solve the problem.

👇 Image
Read 7 tweets
Santiago Profile picture
28 Sep
The 2nd lesson I'd teach anyone learning Python 🐍 will be about using Virtual Environments.

Surprisingly, I've found out that many developers learn about this well into their careers (like I did.)

Here is everything you need to know about Virtual Environments:

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In short, Virtual Environments let you deal with dependencies. Basically, these are the libraries that your code needs to function.

(Who remembers "dll hell" back in the day when working with Microsoft products? If you don't, Google the term.)

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Imagine that you are building your first Python project. You want to load the content of a URL from your code, so you install the "requests" library.

Easy peasy to do using "pip install requests"! See attached image: version 2.24.0 is installed.

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Read 9 tweets
Santiago Profile picture
28 Sep
Python 🐍 is full of surprises!

It's one of those languages with a strong personality that always finds a way to stand up.

Here are 10 interesting —and probably surprising— Python features.

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1⃣Finding how much memory is taken by an object.

This comes in handy whenever you are trying to track down why your program is eating all your available RAM.

I'm constantly dealing with multi-dimensional arrays that eat a lot of memory, so sys.getsizeof() is 👍.

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2⃣Swapping variables in place.

Swapping the value of two variables is one of those things that you learn in the second week of your programming career.

And it's always the same: use a temporal variable to make the swap.

Not with Python.

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Read 12 tweets
Santiago Profile picture
27 Sep
Here is an interesting debate about whether you are ready to be a "Software Developer".

I'll link here to Stas (@StasKlymenko) answer to Mads (@madsbrodt) post, so you should be able to find the entire context before reading what I think.



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Mads' intention is to make people confident in their skills. We all know that imposter syndrome is a real headache, and Mads is motivating people to look past that and stop doubting themselves.

This is really important, especially for people that are starting out.

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Stas' intention is to avoid unwarranted confidence that can lead to frustration and confusion.

Some people watch a 2-hour video, copy working code from the Internet, and before you know it, submit their resume for a full-stack developer position.

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Read 11 tweets
Santiago Profile picture
26 Sep
I do a lot of Python 🐍, but you knew that already, right?

However, there's something new: the libraries and tools that I use on every single project. Every. Single. One.

Here are 10 of them. The best ones.

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1⃣bpython — A lightweight interpreter

I constantly use the interpreter to test ideas or review the syntax of what I want to do.

bpython is a great alternative to the standard REPL. It offers syntax highlighting, expected parameter list, auto-indentation, and autocompletion.

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2⃣rope — A refactoring library

A free utility for refactoring Python code. I personally use it integrated with @code, but you can also use it directly by writing scripts.

This @realpython article discusses rope and refactoring in general: realpython.com/python-refacto…

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Read 12 tweets

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