> Machine learning is the process of making a program which allows a computer to learn from data.
The data could be anything, images, audio or even text.
Here's what you'll hear when you first get into machine learning.
- Neural networks
- Loss
- Weights
- Biases
- Epochs
- Neurons
- Optimizers
...
It can get very confusing really fast!
Here are some of the terms you should know about.
(I wish I had this before)
🧵👇
> Machine learning is the process of making a program which allows a computer to learn from data.
The data could be anything, images, audio or even text.
> Neural Networks are a digital imitation of the neurons you see in the human brain.
> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.
It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.
Each dial's numeric value is dependent on a "weight" and a "bias". The weight determines how important the neuron is and the bias make it flexible.
The neural net is the brain of the machine learning model, the dials you have to adjust to make that neural net work are the neurons.
That was a lot! Let's summarize👇
Neuron : Each dial in a neural network
Weight : How important the neuron is
Bias : Flexibility of neuron
Loss : How wrong the neural net is
Optimizer : Tries to reduce loss and make the neural net less wrong
> How to get started with machine learning?
Here👇
https://t.co/s5o54jt5oc
Here's what your first 30 days of Machine Learning should look like.
— Pratham Prasoon (@PrasoonPratham) November 22, 2020
(I wish I had this before)
\U0001f9f5\U0001f447 pic.twitter.com/7LsR7VsKB8
> Python is the most common and well known, It would be my pick.
https://t.co/sey373KUpV
If all you know is "Hello world" in Python \U0001f40d , here are the programming concepts you need to know to level-up your skills for machine learning.
— Pratham Prasoon (@PrasoonPratham) November 25, 2020
(and resources that will help you)
\U0001f9f5\U0001f447 pic.twitter.com/xaO6PlE0Rc
> You don't need one, use Google Colab.
https://t.co/ATd8YNZppb
Well, you don't even need a PC for getting started with machine learning.
— Pratham Prasoon (@PrasoonPratham) November 26, 2020
With the help of google colab, I am able to train a neural net using a cloud GPU on this phone for free.
What excuse do you have now? pic.twitter.com/RWHSXtnRSy
More from Pratham Prasoon
A list of my favourite tutorials for learning Python as a beginner.
🧵 👇🏻
All the tutorials below include the basics like installation, variables etc.
I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.
Before going through these tutorials I would highly suggest you to go through this thread if you are a complete
The Full Python Course from learn with Python with Rune
key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included
Duration: 8
The Python Beginner's course on FreeCodeCamp's YouTube Channel.
Key highlights
- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.
Duration: 4
🧵 👇🏻
All the tutorials below include the basics like installation, variables etc.
I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.
Before going through these tutorials I would highly suggest you to go through this thread if you are a complete
Are you planning to learn Python for machine learning this year?
— Pratham (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
\U0001f9f5\U0001f447
The Full Python Course from learn with Python with Rune
key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included
Duration: 8
The Python Beginner's course on FreeCodeCamp's YouTube Channel.
Key highlights
- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.
Duration: 4
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.
With hard work and determination, anyone can learn to code.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
You May Also Like
Krugman is, of course, right about this. BUT, note that universities can do a lot to revitalize declining and rural regions.
See this thing that @lymanstoneky wrote:
And see this thing that I wrote:
And see this book that @JamesFallows wrote:
And see this other thing that I wrote:
One thing I've been noticing about responses to today's column is that many people still don't get how strong the forces behind regional divergence are, and how hard to reverse 1/ https://t.co/Ft2aH1NcQt
— Paul Krugman (@paulkrugman) November 20, 2018
See this thing that @lymanstoneky wrote:
And see this thing that I wrote:
And see this book that @JamesFallows wrote:
And see this other thing that I wrote:
