Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)
And if you don't follow me, well, you just hurt my feelings.
😜
11 key concepts of Machine Learning.
— Supervised Learning Edition —
🧵👇
Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)
And if you don't follow me, well, you just hurt my feelings.
😜
(Also referred to as "y")
The label is the piece of information that we are predicting.
For example:
- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not
👇
(Also referred to as "x")
These are the input variables to our problem. We use these features to predict the "label."
For example:
- pixels of a picture
- number of bedrooms of a house
- square footage of a house
👇
(This is also known as "examples.")
A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."
👇
Labeled samples are used to train and validate the model. These are usually represented as (x, y), where "x" is a vector containing all the features, and "y" is the corresponding label.
For example, a labeled sample could be:
([3, 2, 1500], 350000)
Unlabeled samples contain features, but they don't contain the label: (x, ?)
We usually use a model to predict the labels of unlabeled samples.
👇
A model defines the relationship between features and the label.
You can think of a model as a set of rules that, given certain features, determines the corresponding label.
For example, given the # of bedrooms, bathrooms, and square footage, we get the price.
👇
Training is a process that builds a model.
We show the model labeled samples during training and allow the model to gradually learn the relationships between features and the label.
👇
Validation is the process that lets us know whether a model is any good.
Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the labels.
👇
Inference is the process of applying a trained model to unlabeled samples to obtain the corresponding labels.
In other words, "inference" is the process of making predictions using a model.
👇
More from Santiago
Free machine learning education.
Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!
Here are 6 of these programs. Pick one and get started!
↓
Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany
Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.
https://t.co/Uxx97WPCfR
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani
This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.
https://t.co/cKzpDOBVl1
Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny
A theoretical course focusing on design principles and best practices to design deep neural networks.
https://t.co/1TFUAIrAKb
Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov
A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.
https://t.co/hD5no8Pdfa
Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!
Here are 6 of these programs. Pick one and get started!
↓
Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany
Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.
https://t.co/Uxx97WPCfR
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani
This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.
https://t.co/cKzpDOBVl1
Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny
A theoretical course focusing on design principles and best practices to design deep neural networks.
https://t.co/1TFUAIrAKb
Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov
A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.
https://t.co/hD5no8Pdfa
More from Machine learning
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.
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.
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I'm going to do two history threads on Ethiopia, one on its ancient history, one on its modern story (1800 to today). 🇪🇹
I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):
The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹
Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹
References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹
I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):
The famous \u201cLucy\u201d, an early ancestor of modern humans (Australopithecus) that lived 3.2 million years ago, and was discovered in 1974 in Ethiopia, displayed in the national museum in Addis Ababa \U0001f1ea\U0001f1f9 pic.twitter.com/N3oWqk1SW2
— Patrick Chovanec (@prchovanec) November 9, 2018
The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹
Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹
References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹
THREAD PART 1.
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.