BC MACHINE LEARNING

Saved by @jacobhtml

$Prashant \U0001f4da$

Prashant 📚
@capeandcode 4 years, 2 months ago 654 views

Save to PDF Share See On Twitter

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.

Advantages over PCA:

▫️ Can learn non-linear transformations, with non-linear activation functions and multiple layers.

▫️ Doesn't have to learn only from dense layers, can learn from convolutional layers too, better for images, videos right?

▫️ More efficient to learn several layers with auto-encoders rather than one huge transformation with PCA

▫️ Can make use of pre-trained layers from another model to apply transfer learning to enhance the encoder /decoder

Some Common Applications:

🔸 Image Colouring
🔸 Feature Variation
🔸 Dimensionality Reduction
🔸 Denoising Image
🔸 Watermark Removal

Some famous types of autoencoders:

🔹 Convolution Autoencoders
🔹 Sparse Autoencoders
🔹 Deep Autoencoders
🔹 Contractive Autoencoders

Here's the first implementation that I did for dimensionality reduction a couple years, minimal code.
🔗https://t.co/AfAdbA6zMi

More from Machine learning

Santiago
@svpino

For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: 🧵👇

[2] Virtual Environments let you deal with the dependencies that your code has with external Python libraries.

It avoids having conflicts when your projects depend on different versions of the same library.

👇

[3] Let's imagine that you are building your first Python project and you install the "requests" library:

pip install requests

You get version 2.24.0 installed in your system.

👇

[4] A month later, you decide to work on your second project. It also needs the "requests" library.

But the latest version is not 2.24.0 anymore.

Now version 3 is available, and that's the one you want to use!

👇

[5] You could upgrade your entire system to version 3, but then you'll be potentially breaking the first project you built that depends on 2.24.0!

Can you imagine this happening on a server with many more applications running?

👇

Santiago
@svpino

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.

(2 / 16)

Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

(3 / 16)

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.

(4 / 16)

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.

(5 / 16)

Advertisement

$Pratham Prasoon \U0001f680$

Pratham Prasoon 🚀...
@PrasoonPratham

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)

(1 / 22)
🧵👇

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

(2 / 22)

Here's what I did use:

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

(3 / 22)

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

(4 / 22)

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

(5 / 22)

Madison Kanna
@Madisonkanna

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.

Santiago
@svpino

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.

😜

1. Labels

(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

👇

2. Features

(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

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇

You May Also Like

Pat Walls
@thepatwalls

And here they are...

THE WINNERS OF THE 24 HOUR STARTUP CHALLENGE

Remember, this money is just fun. If you launched a product (or even attempted a launch) - you did something worth MUCH more than $1,000.

#24hrstartup

The winners 👇

#10

Lattes For Change - Skip a latte and save a life.

https://t.co/M75RAirZzs

@frantzfries built a platform where you can see how skipping your morning latte could do for the world.

A great product for a great cause.

Congrats Chris on winning $250!

#9

Instaland - Create amazing landing pages for your followers.

https://t.co/5KkveJTAsy

A team project! @bpmct and @BaileyPumfleet built a tool for social media influencers to create simple "swipe up" landing pages for followers.

Really impressive for 24 hours. Congrats!

#8

SayHenlo - Chat without distractions

https://t.co/og0B7gmkW6

Built by @DaltonEdwards, it's a platform for combatting conversation overload. This product was also coded exclusively from an iPad 😲

Dalton is a beast. I'm so excited he placed in the top 10.

#7

CoderStory - Learn to code from developers across the globe!

https://t.co/86Ay6nF4AY

Built by @jesswallaceuk, the project is focused on highlighting the experience of developers and people learning to code.

I wish this existed when I learned to code! Congrats on $250!!

$Arif Hasan\U0001f1ee\U0001f1f3$

Arif Hasan🇮🇳...
@arifhasan18

5 star RSI Strategy by @VishalMalkan

Unconventional approach of 60-40 RSI and range shift concept.

According to him,
whenever the market is bearish,
it won’t be able to cross 60,
and whenever in a bullish trend,
it won’t be able to cross 40.

Like & Retweet for Maximum Reach.

So, instead of the 70-30 Relative Strength Index,
we will use the 60-40 range for the RSI indicator for this strategy.

When the
Monthly and Weekly RSI>60,
& when the RSI takes support at 40 in the daily chart,
it may bounce back and most probably it will not go below 40.

If a bullish candle is formed at the 40 RSI level,
the prices may bounce back, and one can enter the stock.

The stoploss can be placed at the low of that bullish candlestick,
and the target can be the 60 RSI level.

When the
Monthly and Weekly RSI<40,
& when the RSI takes resistance at 60 in the daily chart,
it may bounce back and most probably it will not go above 60

If a bearish candle is formed at the 60 RSI level,
the prices may bounce back from there, & 1 can enter the stock 4 shorting

The stoploss can be placed at the high of that bearish candlestick, and the target can be the 40 RSI levels.

#banknifty

This range shift concept can be applied in any timeframe,
whether intraday, daily,weekly, etc.

Advertisement

$Antonio Garc\xeda Mart\xednez$

Antonio García Martíne...
@antoniogm

Ok, I'll hop on this Elizabeth Warren/Native American testing bandwagon (gently and non-politically).

I've been a @23andMe customer for a while, and have followed their ancestry updates closely.

All is more or less as expected....except for this bit about Native American.

The family is almost completely composed of Spanish peasants (from various regions) who emigrated, along with a massive wave in the late 19th-cent./early 20th-cent., to Cuba back when it was a booming economy (richer than Spain's) and worth emigrating to (Communism killed that).

Also, the native population of Cuba was annihilated early on---was the first place the Spanish colonized after all. Having a native background in Cuba would be like having the same in, say, Massachusetts, particularly if you're (say) mostly Irish. Just really, really unlikely.

(Note: the North African/Arab background is less mysterious. The Iberian peninsula was part of the Muslim world for centuries. It would be odd *not* to have some Arab/Middle Eastern background coming from Spain. Given the family is mostly from Northern Spain, it's small though.)

I have a Spanish passport, have been back to the ancestral villages in Spain, seen the church where my grandmother was baptized, my grandfather told me stories about growing up as the child of Spanish colonists in rural Cuba. The native bit just clashes with all the family lore.

Mystery Grove Publishi...
@MysteryGrove

By popular demand, here is the official Mystery Grove Film Recommendations list. Watch all of these in order if you want to truly understand the world we live in today.

$Dani\xeblle Stotijn$

Daniëlle Stotijn
@DDay35962863

NESARA / GESARA explained for all who are not aware.

"The End of Poverty, The End of Debt, The Beginning of a New Golden Age!"

No Poverty, No Hunger, No Debt, Only Global Prosperity and Peace for ALL

NESARA will merge with GESARA

N = National (US)
G = Global