It avoids having conflicts when your projects depend on different versions of the same library.
👇
For a long time, I didn't understand how to use Virtual Environments in Python 🐍.
If this is just, let's end it here and now: 🧵👇
It avoids having conflicts when your projects depend on different versions of the same library.
👇
pip install requests
You get version 2.24.0 installed in your system.
👇
But the latest version is not 2.24.0 anymore.
Now version 3 is available, and that's the one you want to use!
👇
Can you imagine this happening on a server with many more applications running?
👇
The first step for every new project is to create a virtual environment for it.
Some people have a central location where they store all environments. I prefer to keep them inside the project folder.
👇
python3 -m venv .myvenv
Then, you can use "source" to activate the environment.
At this point, you'll have full isolation for your project.
👇
And this is great!
Here is a @realpython's article covering virtual environments: https://t.co/lgXqJDUlKw
More from Santiago
Here is a simple example of a machine learning model.
I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.
It's TensorFlow and Keras.
If you are starting out, this may be a good puzzle to solve.
The goal of this model is to learn to multiply one-digit
I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.
It's TensorFlow and Keras.
If you are starting out, this may be a good puzzle to solve.
The goal of this model is to learn to multiply one-digit
It is a good example of coding, what is the model?
— Freddy Rojas Cama (@freddyrojascama) February 1, 2021
More from Machine learning
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
