The 8-step quick-start guide to learn Machine Learning.

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I told everyone that I didn't care.

"Screw math! I've never been great with it, so I'm not starting with machine learning to fail at the end."

That was many years ago.

Math is still hard, but I don't think you should be scared at all. Here is why: 🧵👇

[1] One thing changed my mind: school pushed me to the deep end of the pool, and I was forced to swim.

I had to face my fears, and I started machine learning and realized that the math involved is not as scary (or as much) as some people believe.

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[2] Probably one of the most frequent questions I get is around the math needed for machine learning.

Answer:

▫️ Probabilities and Statistics
▫️ Linear Algebra
▫️ Calculus

But it turns out that this is not helpful.

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[3] What are you gonna do with this?

Are you going to learn all there's is about linear algebra before looking into machine learning?

How about probabilities and statistics? Or calculus? Do you know how extensive calculus is?

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[4] Many people stop looking into machine learning as soon as somebody tells them that calculus is a pre-requisite.

First of all, the amount of calculus you truly need is minuscule.

Second, without proper direction, there's no way you'll find your way through the noise.

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I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.

Do you know what's better than a machine learning model?

Two models.

More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.

But there's a problem with this approach: ↓

I'm gonna focus here on image classification.

Let's assume you built two different models:

• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)

They both solve the same problem, so you want to combine their results to pick the right answer.

The problem is that you have two models, so voting is not trivial.

What happens in this case?

• Model 1's answer: Class A
• Model 2's answer: Class B

Which one do you select?

Notice that this problem is not limited to an even number of models.

You could have 3 models, each giving you a different answer.

How do you decide which answer to choose?

There are multiple ways to approach this problem. I'll mention a few different ideas on this thread.

Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.

A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5

"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

I've applied for a total of 10 development jobs in my life:

- 7 lead to interviews
- Of those 7, 4 was a good fit for me
- Of those 4, 3 offered me the job
- In all 3 cases, I negotiated a better deal

🧵 on what worked for me and what YOU can do to get similar results 👇

1. Apply for the RIGHT jobs. Look for midsize to large companies, not necessarily tech related. These tend to get way fewer applications than traditional tech companies, so it's easier to get your foot in the door - and they also tend to be great environments for learning.

2. Spend time on your application. Make sure your resumé and written application is tailored to the exact position. Recruiters or hiring managers can smell a copy/pasted resumé from miles away. Use as many words from the job spec in your application as possible.

3. Have a portfolio. Show off your skills and some personality on your website. This is a nobrainer for front-end devs, but it's true for other areas too. It's a great way to stand out over other candidates by actually *showing* the value you can create and your experience.

4. Establish personal contact. Send a quick message or make a phone call before applying. Just introduce yourself to the person in charge of hiring and ask a simple question. It's the best way I find to make them remember your application when you submit it.

Ever heard of Autoencoders?

The first time I saw a Neural Network with more output neurons than in the hidden layers, I couldn't figure how it would work?!

#DeepLearning #MachineLearning
Here's a little something about them: 🧵👇

Autoencoders are unsupervised neural networks whose architecture you can picture as two funnels connect from the narrow ends.

These networks are primary focus for compression tasks of data in Machine Learning.

We feed them the data so that they can learn the most important features, a smaller representation while keep the integrity of the data.

Later when someone needs, can just take that small representation and recreate the original, just like a zip file.📥

Being unsupervised, they require no labels.
Our inputs and outputs are same and a simple euclidean distance can be used as a loss function for measuring the reconstruction.

Of course, we wouldn't expect a perfect reconstruction.

We can think of an autoencoder having two components, encoder and decoder, represented by the below equations:

We are just trying to minimize the L here. All the backpropagation rules still hold.

The "polarization is the problem" narrative to me is bad because it implies the status quo was a stable and acceptable set of trade-offs. It's the equivalent of a pollyanna "why can't we all get along" but the we in question is white supremacy and its targets.

like i understand that conflict mediation requires mutual concessions (i did my MA in conflict studies) and while it's important to meet people's deep needs across spectrums of difference, certain things are just not up for negotiation.

Also while peacebuilding work is good. It's not just "seeing each other as human" it's literally about transforming structural issues. (hence Galtung's distinction between positive and negative peace wherin negative peace is like a ceasefire and positive is a transformed society.

And I think that to whatever extent that kind of peacebuilding work is applicable, it's not between fascists and people who think fascists are bad. Fascists are not invited. They are only invited if they seize territories and can't be crushed. Then secret negotiations.

The US is always like "we don't negotiate with terrorists" but literally every president negotiates with who they consider to be terrorists. It's all just down low. But that's just because counter-insurgency failed.

Are you planning to learning machine learning this year?

These are the Python frameworks you need to learn as an absolute beginner.

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It can be overwhelming seeing all the machine learning frameworks at once and not even knowing what they do.

I made this thread so that your machine learning journey becomes just a little bit easier.

(2 / 11)

This thread will only focus on Python frameworks.

Why?
Because Python is the most common path into machine learning.

( If you have experience with any other language then learning Python will not be difficult at all. )

(3 / 11)

Here's the entire list:

- Pandas
- Numpy
- Matplotlib
- TensorFlow
- PyTorch
- Keras
- Scikit Learn

Let's see what they do.

(4 / 11)

You'll have to interact with csv files or databases on a regular basis to access data in machine learning, pandas helps you do just that.

The cool thing about Pandas is that it actually doesn't change the original data...

(5 / 11

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)

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These terms won't mean anything unless you know what Machine learning is all about.

> 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.

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> 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.

Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.