When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python.
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These are the tools you will need for machine learning in Python.
🧵👇
When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python.
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Meet Anaconda which allows you to run several versions of python. It comes pre-installed with several data science and machine learning frameworks.
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You can use pip env or Anaconda, whichever works for you.
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Jupyter notebooks is an IDE just like VS code or Sublime. The special thing about jupyter is that you can parts of code in mini code editors called cells. This is great for prototyping and testing code.
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Collab is a jupyter notebook running on google's servers which gives you access to GPUs and TPUs for training machine learning models faster for free, yes free.
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I like to call Kaggle the codepen for machine learning and data science.This is the place where you show off you machine learning skills. You have access to datasets for which you can make machine learning models and compete with other people around the world.
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TensorFlow is a framework for machine learning,it has variants like TensorFlow.js for machine learning in the browser, TensorFlow lite for machine learning on mobile phones, and the standard TensorFlow library.
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PyTorch is an open-source machine learning library based on the Torch library,used for applications such as computer vision and natural language processing. It is very similar to TensorFlow in the things you can do in it with differences in the syntax.
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Matplotlib is a library for plotting data into pie charts, bar charts, and whatever kinds of graphs you can imagine.
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Numpy replaces the lists in Python with its lists, but why? Aren't the default lists good enough? The thing is that NumPy lists are much faster than Python lists, hence the wide usage of NumPy.
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More from Pratham Prasoon
This is a step-by-step guide on getting started with Python as a total beginner.
(includes free resources and everything else you need to get started)
🧵👇
Before we begin, I want to congratulate you on your decision to learn how to code using Python.
I still remember how I wrote my first piece of code 6 years and all the amazing and cool things I've been able to do with it ever since.
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Make sure to keep in mind that it is probably best for you to keep your expectations in check.
Don't expect to make AAA games or state of the art machine learning models in a week.
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Programming is not something that you can learn in a single week, it takes consistent effort and dedication over time to get good at it.
With all that being said, let's dive straight in.
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In order to write Python code, you'll need to install Python on your system.
Linux and macOS users can skip this step because they come pre-installed with Python.
Download link: https://t.co/KSZ4Qd6CNk
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(includes free resources and everything else you need to get started)
🧵👇
Before we begin, I want to congratulate you on your decision to learn how to code using Python.
I still remember how I wrote my first piece of code 6 years and all the amazing and cool things I've been able to do with it ever since.
(2 / 24)
Make sure to keep in mind that it is probably best for you to keep your expectations in check.
Don't expect to make AAA games or state of the art machine learning models in a week.
(3 / 24)
Programming is not something that you can learn in a single week, it takes consistent effort and dedication over time to get good at it.
With all that being said, let's dive straight in.
(4 / 24)
In order to write Python code, you'll need to install Python on your system.
Linux and macOS users can skip this step because they come pre-installed with Python.
Download link: https://t.co/KSZ4Qd6CNk
(5 / 24)
More from Machine learning
Do you want to learn the maths for machine learning but don't know where to start?
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
Are you planning to learn Python for machine learning this year?
— Pratham Prasoon (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
\U0001f9f5\U0001f447
These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.
A THREAD 👇
Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.
I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.
Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.
There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.
There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics
Are you not using custom schematics yet? Take a look at this:
https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C
You can automate all the boilerplate with hundreds of files associates with creating a new feature.
A THREAD 👇
Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.
I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.
Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.
There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.
There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics
Are you not using custom schematics yet? Take a look at this:
https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C
You can automate all the boilerplate with hundreds of files associates with creating a new feature.
You May Also Like
A THREAD ON @SarangSood
Decoded his way of analysis/logics for everyone to easily understand.
Have covered:
1. Analysis of volatility, how to foresee/signs.
2. Workbook
3. When to sell options
4. Diff category of days
5. How movement of option prices tell us what will happen
1. Keeps following volatility super closely.
Makes 7-8 different strategies to give him a sense of what's going on.
Whichever gives highest profit he trades in.
2. Theta falls when market moves.
Falls where market is headed towards not on our original position.
3. If you're an options seller then sell only when volatility is dropping, there is a high probability of you making the right trade and getting profit as a result
He believes in a market operator, if market mover sells volatility Sarang Sir joins him.
4. Theta decay vs Fall in vega
Sell when Vega is falling rather than for theta decay. You won't be trapped and higher probability of making profit.
Decoded his way of analysis/logics for everyone to easily understand.
Have covered:
1. Analysis of volatility, how to foresee/signs.
2. Workbook
3. When to sell options
4. Diff category of days
5. How movement of option prices tell us what will happen
1. Keeps following volatility super closely.
Makes 7-8 different strategies to give him a sense of what's going on.
Whichever gives highest profit he trades in.
I am quite different from your style. I follow the market's volatility very closely. I have mock positions in 7-8 different strategies which allows me to stay connected. Whichever gives best profit is usually the one i trade in.
— Sarang Sood (@SarangSood) August 13, 2019
2. Theta falls when market moves.
Falls where market is headed towards not on our original position.
Anilji most of the time these days Theta only falls when market moves. So the Theta actually falls where market has moved to, not where our position was in the first place. By shifting we can come close to capturing the Theta fall but not always.
— Sarang Sood (@SarangSood) June 24, 2019
3. If you're an options seller then sell only when volatility is dropping, there is a high probability of you making the right trade and getting profit as a result
He believes in a market operator, if market mover sells volatility Sarang Sir joins him.
This week has been great so far. The main aim is to be in the right side of the volatility, rest the market will reward.
— Sarang Sood (@SarangSood) July 3, 2019
4. Theta decay vs Fall in vega
Sell when Vega is falling rather than for theta decay. You won't be trapped and higher probability of making profit.
There is a difference between theta decay & fall in vega. Decay is certain but there is no guaranteed profit as delta moves can increase cost. Fall in vega on the other hand is backed by a powerful force that sells options and gives handsome returns. Our job is to identify them.
— Sarang Sood (@SarangSood) February 12, 2020