We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

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One of the arguments for permitting monopolies is that they are "efficient." That's the logic under which Universal was allowed to acquire Comcast and NBC - the "vertical integration" would make all three companies better and we'd all reap the benefit.

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It turns out that there are DISeconomies of scale, what Brandeis called "the curse of bigness" and really, the Universal-NBC-Comcast octopus is a poster child for that

Comcast has just informed its subscribers that they are at risk of losing access to "Bravo, CNBC, E!, Golf Channel, MSNBC, Olympic Channel, Oxygen, Syfy, Telemundo, Universal Kids, NBC Universo, USA Network and NBC Sports

That is, Comcast has warned its customers that Comcast might not license its channels to Comcast anymore.

This is a "carriage dispute" - a dispute over how much the cable operator will pay the broadcaster. It's a common dispute to have.

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But in this case, Comcast's dispute is over the accounting fiction of how much one division of a vertical monopolist will nominally charge another to access its products.

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If you think "It's not censorship unless the government does it," I want to change your mind.

It's absolutely true that the First Amendment only prohibits government action to suppress speech based on its content, but the First Amendment is not the last word on censorship.

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Here are some kinds of private speech-suppression that I think most of us can agree are censorship: when the John Birch Society burned mountains of rock records and novels - or when Tipper Gore's PMRC pressured record stores to drop punk, metal and rap albums.

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Or the Comics Code Authority, which signed up all comics publishers and retailers to block comics if they contained anything unfit for small children, which stunted American comics for generations while their European counterparts created entire sophisticated genres.

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Or MPAA ratings, in which a secret group of censors (falsely described as frequently rotated, randomly selected parents - really they're long-serving studio insiders) decides whether movies get NC-17 ratings and thus be blocked from nearly every screen in the country.

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(You can learn more about this from Kirby Dick's unmissable doc, "This Film Is Not Yet Rated," which documents both how the MPAA misleads the public about ratings, and uses them to block LGBTQ

Here's how I got started with my first machine learning project and you can too.
Let's take a look.

(This thread will take from zero to being a hero in machine learning, trust me)

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🧵👇

Getting started with your first machine learning project might actually much easier than it seems, if I can do it, certainly anyone can.

I did not use:
- Any Math
- An expensive computer
- Complex programming concepts

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Here's what I did use:

- A free GPU on Google Colab
- Python
- TensorFlow
- Numpy
- Pandas
- Kaggle
- Scikit-Learn
- Google
- StackOverFlow

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This project was actually a Kaggle challenge based on the MNIST dataset which is a collection images of 70,000 hand written digits.

You can find the dataset here👇
🔗//kaggle.com/c/digit-recognizer

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Before we go over the code of this project, it is highly reccomended that you complete this free course on YouTube👇

Machine Learning foundations course
🔗//youtu.be/_Z9TRANg4c0

Code👇
🔗//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

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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.

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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1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.

3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*

4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.

5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do: