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.
(2 / 13)
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
(5 / 24)
(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
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So it's now October 10, 2018 and....Rod Rosenstein is STILL not fired.
He's STILL in charge of the Mueller investigation.
He's STILL refusing to hand over the McCabe memos.
He's STILL holding up the declassification of the #SpyGate documents & their release to the public.
I love a good cover story.......
The guy had a face-to-face with El Grande Trumpo himself on Air Force One just 2 days ago. Inside just about the most secure SCIF in the world.
And Trump came out of AF1 and gave ol' Rod a big thumbs up!
And so we're right back to 'that dirty rat Rosenstein!' 2 days later.
At this point it's clear some members of Congress are either in on this and helping the cover story or they haven't got a clue and are out in the cold.
Note the conflicting stories about 'Rosenstein cancelled meeting with Congress on Oct 11!"
First, rumors surfaced of a scheduled meeting on Oct. 11 between Rosenstein & members of Congress, and Rosenstein just cancelled it.
He's STILL in charge of the Mueller investigation.
He's STILL refusing to hand over the McCabe memos.
He's STILL holding up the declassification of the #SpyGate documents & their release to the public.
I love a good cover story.......
The guy had a face-to-face with El Grande Trumpo himself on Air Force One just 2 days ago. Inside just about the most secure SCIF in the world.
And Trump came out of AF1 and gave ol' Rod a big thumbs up!
And so we're right back to 'that dirty rat Rosenstein!' 2 days later.
At this point it's clear some members of Congress are either in on this and helping the cover story or they haven't got a clue and are out in the cold.
Note the conflicting stories about 'Rosenstein cancelled meeting with Congress on Oct 11!"
First, rumors surfaced of a scheduled meeting on Oct. 11 between Rosenstein & members of Congress, and Rosenstein just cancelled it.
Rep. Andy Biggs and Rep. Matt Gaetz say DAG Rod Rosenstein cancelled an Oct. 11 appearance before the judiciary and oversight committees. They are now calling for a subpoena. pic.twitter.com/TknVHKjXtd
— Ivan Pentchoukov \U0001f1fa\U0001f1f8 (@IvanPentchoukov) October 10, 2018
1/ Some initial thoughts on personal moats:
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
I'm increasingly interested in the idea of "personal moats" in the context of careers.
— Erik Torenberg (@eriktorenberg) November 22, 2018
Moats should be:
- Hard to learn and hard to do (but perhaps easier for you)
- Skills that are rare and valuable
- Legible
- Compounding over time
- Unique to your own talents & interests https://t.co/bB3k1YcH5b
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
People talk about \u201cpassive income\u201d a lot but not about \u201cpassive social capital\u201d or \u201cpassive networking\u201d or \u201cpassive knowledge gaining\u201d but that\u2019s what you can architect if you have a thing and it grows over time without intensive constant effort to sustain it
— Andrew Chen (@andrewchen) November 22, 2018
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
Things that look like moats but likely aren\u2019t or may fade:
— Erik Torenberg (@eriktorenberg) November 22, 2018
- Proprietary networks
- Being something other than one of the best at any tournament style-game
- Many "awards"
- Twitter followers or general reach without "respect"
- Anything that depends on information asymmetry https://t.co/abjxesVIh9
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
