So how does that work?
The first thing to know is that everything in Python is an object - functions too
🤔 Python decorators
What are they? How do you use them?
🧵 Let's find out 👇
So how does that work?
The first thing to know is that everything in Python is an object - functions too
Functions that take other functions as an argument are called higher-order functions
log_enter_leave is a decorator.
my_function is the function.
To alter my_function's behavior we reassign it applying log_enter_leave decorator.
A "pie-decorator" syntax using @
@decorator_name
To solve that we just need to use wraps from functools
For example, you can dataclass decorator on your class to automatically generate its __init__ and __repr__ methods
More from Machine learning
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
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
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.
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There are many strategies in market 📉and it's possible to get monthly 4% return consistently if you master 💪in one strategy .
One of those strategies which I like is Iron Fly✈️
Few important points on Iron fly stategy
This is fixed loss🔴 defined stategy ,so you are aware of your losses . You know your risk ⚠️and breakeven points to exit the positions.
Risk is defined , so at psychological🧠 level you are at peace🙋♀️
How to implement
1. Should be done on Tuesday or Wednesday for next week expiry after 1-2 pm
2. Take view of the market ,looking at daily chart
3. Then do weekly iron fly.
4. No need to hold this till expiry day .
5.Exit it one day before expiry or when you see more than 2% within the week.
5. High vix is preferred for iron fly
6. Can be executed with less capital of 3-5 lakhs .
https://t.co/MYDgWkjYo8 have R:2R so over all it should be good.
8. If you are able to get 6% return monthly ,it means close to 100% return on your capital per annum.
One of those strategies which I like is Iron Fly✈️
Few important points on Iron fly stategy
This is fixed loss🔴 defined stategy ,so you are aware of your losses . You know your risk ⚠️and breakeven points to exit the positions.
Risk is defined , so at psychological🧠 level you are at peace🙋♀️
How to implement
1. Should be done on Tuesday or Wednesday for next week expiry after 1-2 pm
2. Take view of the market ,looking at daily chart
3. Then do weekly iron fly.
4. No need to hold this till expiry day .
5.Exit it one day before expiry or when you see more than 2% within the week.
5. High vix is preferred for iron fly
6. Can be executed with less capital of 3-5 lakhs .
https://t.co/MYDgWkjYo8 have R:2R so over all it should be good.
8. If you are able to get 6% return monthly ,it means close to 100% return on your capital per annum.
