BC MACHINE LEARNING

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Jan Giacomelli
@jangiacomelli 4 years, 6 months ago 951 views

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🤔 Python generators

What are they? How to use them?

#Python

🧵Let's find out 👇

1️⃣ Python generators are lazy iterators delivering the next value when their .next() is called.

They are created by using the yield keyword

next() can be called explicitly or implicitly inside for loop

They can be finite or infinite

2️⃣ yield - where a value is sent back to the caller, but the function doesn’t exit afterward as with the return statement

The state of function is remembered.

For example, the number is incremented and sent back from yield at the consecutive call next()

3️⃣ Generator stores only the current state of the function - it generates next element on next() call and forgets the previous one -> it saves memory

For example, we don't need to store 1mio elements in memory to do something with each element

4️⃣ When you call a generator function generator object is returned - it's not executed yet

It executes only when next() is called

For example, that's why Exception is raised only on the next() call

5️⃣ When the generator goes out of elements it raises StopIteration exception

6️⃣ You can also create a class that behaves like a generator - it needs implemented methods:

- __iter__ -> to enable iteration
- __next__ -> to enable next element access

7️⃣ You can also create a generator with one-liner expression similar to list comprehension

8️⃣ You can read more:

https://t.co/mXp7L8l2Ai

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Pratham Prasoon
@PrasoonPratham

Are you planning to learn 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)

Pratham Prasoon
@PrasoonPratham

Machine Learning for the Web developer in 2021.

The beginner's guide.

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

(2 / 14)

"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

(3 / 14)

Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

(4 / 14)

In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

(5 / 14)

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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.

👇

Anson Horton
@AnsonHorton

1/ It's probably not the first thing you think of, but when we started .NET (COM+) in the late 90s, C# didn't exist yet. We were working on it at the same time as the CLR and the framework. So, you might wonder, what language was being used to generate IL and write the BCL?

2/ The answer is a language that we called SMC that Peter Kukol wrote the compiler for. Peter is a flat out amazing engineer and wrote the core parts of the compiler in just a few days. This unblocked the framework team, allowed vetting the runtime and interpreter, etc.

3/ SMC was a trimmed down C++ variant and the compiler was written in itself (i.e. SMC). It didn't support things like destructors, multiple inheritance, virtual base classes, etc. But, overall it enabled progress that would have otherwise been stalled.

4/ It did introduce a problem for the C# language and compiler team though. Every day more and more code was written in SMC, a language that we did not intend to ship externally. Designing a new language is hard (well, at least, a general purpose one that becomes popular).

5/ So there was a constant balancing act between laying enough of the foundation of the language and the C# compiler to replace SMC and making sure that the design lived up to the goals and ideals the team was striving for. If we waited too long to adopt C#,

$Prashant \U0001f4da$

Prashant 📚
@capeandcode

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.

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Daddy Files
@DaddyFiles

This is my son, Sam. He’s 5. And today he learned how shitty and harmful #ToxicMasculinity is. My rage meter is spiking right now so excuse me if this is a little raw but there are some things I want to say about BS #gender norms (a thread)

Sam is my middle child & he’s a terror. A “boy’s boy” as so many (not me) would say. He’s rough and tumble, he’s loud, he’s always dirty, loves trucks, plays sports and knee drops me from the couch. But he also loves a lot of “girl” things.

Sam has a collection of purses because he likes to carry things around. And he also loves to have his nails painted bright colors because he thinks they “look beautiful.” And he’s right – they are beautiful…

So he proudly wore his red nail polish to kindergarten this morning because Sam has absolutely no concept of nail polish only being for girls or reason to think anyone would possibly have a problem with beautiful nails.

But his classmates did have a problem. A big one. Sam was ridiculed for being a boy with nail polish. They called him names and told him to take it off. This lasted the entire day.

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Aditya Todmal
@AdityaTodmal

A THREAD ON @SarangSood

Decoded his way of analysis/logics for everyone to easily understand.

Have covered:
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2. Workbook
3. When to sell options
4. Diff category of days
5. How movement of option prices tell us what will happen

1. Keeps following volatility super closely.

Makes 7-8 different strategies to give him a sense of what's going on.

Whichever gives highest profit he trades in.

I am quite different from your style. I follow the market's volatility very closely. I have mock positions in 7-8 different strategies which allows me to stay connected. Whichever gives best profit is usually the one i trade in.
— Sarang Sood (@SarangSood) August 13, 2019

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This week has been great so far. The main aim is to be in the right side of the volatility, rest the market will reward.
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4. Theta decay vs Fall in vega

Sell when Vega is falling rather than for theta decay. You won't be trapped and higher probability of making profit.

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Learning @ Prakash
@Prakashplutus

My simple Approach towards Breakout trading :

1. Scan stocks on the basis of FTDB Scanner.

2. Buy stocks with EMA-9> EMA-21> EMA-63 > EMA 200 & Volume 200% up

3. maximum SL of 1.25X ( ATR )

4. I do breakout trading only in Nifty -500 stocks

#StockMarket
#trading
#Nifty