BC MACHINE LEARNING
Saved by @Alex1Powell

An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

 Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

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The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

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The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

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The data type of a tensor refers to the type of data contained in it.

For example, when thinking about Python 🐍's numpy library, here are some of the supported data types:

▫️ float32
▫️ float64
▫️ uint8
▫️ int32
▫️ int64

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In the previous tweets I used the terms "vector" and "matrix," to referr to tensors with a specific rank (1 and 2 respectively.)

We can also use these mathematical concepts when describing tensors.

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A scalar —or a 0D tensor— has rank 0 and contains a single number. These are also called "0-dimensional tensors."

The attached image shows how to construct a 0D tensor using numpy. Notice its shape and its rank (.ndim attribute.)

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A vector —or a 1D tensor— has rank 1 and represents an array of numbers.

The attached image shows a vector with shape (4, ). Notice how its rank (.ndim attribute) is 1.

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A matrix —or a 2D tensor— has rank 2 and represents an array of vectors. The two axes of a matrix are usually referred to as "rows" and "columns."

The attached image shows a matrix with shape (3, 4).

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You can obtain higher-dimensional tensors (3D, 4D, etc.) by packing lower-dimensional tensors in an array.

For example, packing a 2D tensor in an array gives us a 3D tensor. Packing this one in another array gives us a 4D tensor, and so on.

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Here are some common tensor representations:

▫️ Vectors: 1D - (features)
▫️ Sequences: 2D - (timesteps, features)
▫️ Images: 3D - (height, width, channels)
▫️ Videos: 4D - (frames, height, width, channels)

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Commonly, machine learning algorithms deal with a subset of data at a time (called "batches.")

When using a batch of data, the tensor's first axis is reserved for the size of the batch (number of samples.)

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For example, if your handling 2D tensors (matrices), a batch of them will have a total of 3 dimensions:

▫️ (samples, rows, columns)

Notice how the first axis is the number of matrices that we have in our batch.

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Following the same logic, a batch of images can be represented as a 4D tensor:

▫️ (samples, height, width, channels)

And a batch of videos as a 5D tensor:

▫️ (samples, frames, height, width, channels)

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If all of this makes sense, you are on your way! If something doesn't click, reply with your question, and I'll try to answer.

Either way, make sure to follow me for more machine learning content! 2021 is going to be great!

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More from Santiago

Santiago
Santiago
@svpino
10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB


2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5


3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB


4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm
MACHINE LEARNING
Santiago
Santiago
@svpino
Here is a simple example of a machine learning model.

I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.

It's TensorFlow and Keras.

If you are starting out, this may be a good puzzle to solve.


The goal of this model is to learn to multiply one-digit
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Santiago
Santiago
@svpino
A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

• Class A: 90% accuracy
• Class B: 80% accuracy
• Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ↓

Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.

How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

• Class A: 95% accuracy
• Class B: 100% accuracy
• Class C: 71% accuracy

Do you see what just happened?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you 😉.
ALL
Santiago
Santiago
@svpino
20 machine learning questions that will make you think.

(Cool questions. Not the regular, introductory stuff that you find everywhere.)

🧵👇

1. Why is it important to introduce non-linearities in a neural network?

2. What are the differences between a multi-class classification problem and a multi-label classification problem?

3. Why does the use of Dropout work as a regularizer?

👇

4. Why you shouldn't use a softmax output activation function in a multi-label classification problem when using a one-hot-encoded target?

5. Does the use of Dropout in your model slow down or speed up the training process? Why?

👇

6. In a Linear or Logistic Regression problem, do all Gradient Descent algorithms lead to the same model, provided you let them run long enough?

7. Explain the difference between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.

👇

8. What are the advantages of Convolution Neural Networks (CNN) over a fully connected network for image classification?

9. What are the advantages of Recurrent Neural Networks (RNN) over a fully connected network when working with text data?

👇
ALL , MACHINE LEARNING
Santiago
Santiago
@svpino
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.
DS

More from Machine learning

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

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇
MACHINE LEARNING , STARTING MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Which libraries do you really need to get started with Machine Learning and why?

🧵👇

First and foremost, make sure that you have nailed the fundamental concepts of Python because machine learning requires a lot of programming!

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If you know these topics, then you are good to go with machine learning in Python

- Object-oriented programming in Python:Classes,Objects,Methods
- Lists & List functions
- List comprehension
- List slicing
- String formatting
- List,Dictionaries & Tuples

(3 / 19)

Now, let's understand what popular python machine learning libraries do.

We will talk about👇
- TensorFlow (+ Keras)
- PyTorch
- Pandas
- Numpy
- Matplotlib
- SciKit Learn
- Seaborn

(4 / 19)

Now let's cover the libraries that you have to learn (at least the basics) for machine learning

1. Pandas

Pandas is a python library that allows you to store and read data from spreadsheets ( .csv, .xlsv files ) in structures called Dataframes.

(5 / 19)
MACHINE LEARNING
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John Burn-Murdoch
John Burn-Murdoch
@jburnmurdoch
Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learn to Python for machine learning this year?

Here's everything you need to get started.
🧵👇

In this thread we'll look at all the concepts in Python you need to know for machine learning along with free resources to help you out.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19

You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
MACHINE LEARNING , PYTHONML
Mads Brodt
Mads Brodt
@madsbrodt
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.
MACHINE LEARNING

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Joey Tawadrous 🇮🇪...
@joeytawadrous
1) Core attributes of a promising founder worth following:
- Progress/trajectory over time
- How well they lead a team &
- Make the vision happen
- Most impressed when someone is way ahead of where you thought they would be when you check up/see them a few months later.

2) Indie Makers have such a benefit of being small, fast, nimble workers in comparison to big companies.

We can easily pivot and vastly change a project of ours. We can change as we learn more about our users.

Big companies can take months/years for even the smallest of pivots.

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Then you only have one thing to do: scale, scale, scale.

4) Be obsessed with the little details.
- listen to every single person who uses your product
- reply to every single person who emails you about your product
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- iterate your product, make changes, pivot if necessary

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In the early stages of a company just;
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Lindsey Simmons
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@LynzforCongress
Joshua Hawley, Missouri's Junior Senator, is an autocrat in waiting.

His arrogance and ambition prohibit any allegiance to morality or character.

Thus far, his plan to seize the presidency has fallen into place.

An explanation in photographs.

🧵

Joshua grew up in the next town over from mine, in Lexington, Missouri. A a teenager he wrote a column for the local paper, where he perfected his political condescension.

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This is a piece of his history he works to erase as he builds up his counterfeit image as a rural farm boy from a small town who grew up farming.

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Maybe more geeks need to meet someone who says “My job is teleporting value across time and space.”

Financial engineering is sort of a squishy term. I don’t primarily mean payments infrastructure or financial rails, although both of those involve substantial engineering.

I mean more “We took a thing and performed some alchemy, and now have a new thing.”

Except instead of alchemy it is generally quite a bit of math, a huge amount of contract law and due diligence, often an impressive amount of ops work, and a dusting of technology that web devs would recognize as such.
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Simon DeDeo
@SimonDeDeo
So, today, for the first time in 25 (!) years of Apple, I downgraded. From the 2016 MacBook Pro to my 2013, which I had kept in a drawer...


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TECH