BC MACHINE LEARNING

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Santiago
@svpino 5 years, 7 months ago 1081 views

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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

5⃣ Numpy is another Python library that you will use every single day.

@keithgalli's "Complete Python NumPy Tutorial" is a great start.

https://t.co/Xg0YbuR8fz

6⃣ One of the most basic algorithms that you can learn is Decision Trees.

Watch @random_forests' video where he builds a decision tree from scratch:

https://t.co/tKtUpO1K3l

7⃣ It's hard to talk about machine learning without touching on neural networks.

Probably the best video out there that explains how neural networks work is @3blue1brown's:

https://t.co/OMJHiG7PIu

8⃣ Scikit-Learn is one of the most popular machine learning libraries out there.

@simplilearn's "Scikit-Learn Tutorial" is a great place to start.

https://t.co/efd1kmz07c

9⃣ TensorFlow is the most popular deep learning library that's currently used in the industry.

Here is a massive 7-hour tutorial of TensorFlow 2.0 produced by @freeCodeCamp.

https://t.co/BYUoAQJEeu

🔟 Finally, a great way to start getting familiar with machine learning is the bite-sized recipes published by Google.

This series is worth every minute.

Playlist: https://t.co/xDqhmNQoWg

If you are looking for real-life, hands-on information related to machine learning, follow me.

✌️

If you have questions or suggestions about topics you'd like to hear about, let me know.

More from Santiago

Santiago
@svpino

My recommendation to start learning how to deal with structured data: Kaggle's Pandas Tutorial.

• Creating, Reading, Writing
• Indexing, Selecting, Assigning
• Summary Functions, Maps
• Grouping, Sorting
• Data Types, Missing Values
• Renaming, Combining

100% Python 🐍.

Santiago
@svpino

Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇

Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]

What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]

What kind of problems have you worked on in the past?

Can you list the specific use cases related to each one of these?

[4 / 10]

What is the most exciting project you have ever worked on?

What was your role and responsibilities on that project?

Why do you think your work on that project was important?

[5 / 10]

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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)

Santiago
@svpino

Some of my machine learning books.

Hands-On Machine Learning from @aureliengeron and Deep Learning with Python from @fchollet.

Santiago
@svpino

Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.

More from Machine learning

Alejandro Piad Morffis
@AlejandroPiad

This is a Twitter series on #FoundationsOfML.

❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.

This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵

Last time we talked about how Machine Learning works.

Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric

I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.

\u2753 What is Machine Learning?

This is my preferred way of explaining it... \U0001f447\U0001f9f5
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021

According to the nature of that experience, we can define different formulations, or flavors, of the learning process.

A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇

1️⃣ Supervised Learning

In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.

👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.

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?

👇

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$Prashant \U0001f4da$

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.

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)

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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Jaya_Upadhyaya
@Jayalko1

HORANADU ANNAPOORNESHWARI, CHIKMAGALUR (KAR)
#bharatmandir

Annapoorneshwari is the Devi of nutrition and food. The literal meaning of Annapoorneshwari is “Feeding one and all”. Anyone who comes here is offered meals and shelter within the premises.
@LostTemple7

Maharshi Agastya is believed to have done pratishtha here.

Once, Shiva and Parvati had an argument. Shiva declared everything in the world including food to be Maya or illusion. To prove him wrong, Devi Parvati disappeared resulting in entire Earth becoming still.

Taking pity, Devi appeared as Annapoorna and relieved everyone.
Another belief is that once Shiva beheaded Brahma and his skull got stuck in Shiva’s hand. He was cursed that till the skull is not full of food or grains, it will remain stuck to his hands.

Shiva went everywhere and asked for food but the skull was never full. He finally came here and Maa Annapoorna filled the skull with grains and reversed Shiva’s curse.

It is believed that who ever visits Devi Annapoorna here will never be without food in their lives.

WORLD OF SANATAN DHARM...
@world_sanatan

MINUTE DETAILS ABOUT THE ATTACK ON SHAISTA KHAN AT LAL MAHAL

Remarkable Commando op

A long 🧵 but worth your time

Aurangzeb sent his maternal uncle Shaista Khan with powerful army over 1,50,000 on the request of Badibegum Sahiba, Adilshahi sultanate

1/

Shaista Khan entered Deccan to kill Chhatrapati Shivaji Maharaj & to destroy his empire. Shahista Khan captured a lot of province and fort of Chakan near Pune.

2/

Till 3 years he gave lot of troubles to the subjects of Chhatrapati Shivaji Maharaj and he personally made Lal Mahal his destination to live at , which was the property of Chhatrapati Shivaji Maharaj . Lal Mahal, where Chhatrapati Shivaji Maharaj spent his childhood.

3/

1. In the April 1663 Chhatrapati Shivaji Maharaj personally made a surprise attack on Shahista Khan in the Lal Mahal Pune.

2. Chhatrapati Shivaji Maharaj attacked with 300 soldiers at the midnight while Lal Mahal had strong security of 1.5 lac soldiers for Shahista Khan.

4/

3. He entered in Pune, he made a drama of wedding ceremony to enter in Pune near Lal Mahal.

4. During the night they made big hole at the wall of Lal Mahal and entered into the palace.

5/

$Sam Dylan Finch \U0001f937\U0001f3fb\u200d\u2642\ufe0f$

Sam Dylan Finch 🤷🏻‍♂️...
@samdylanfinch

I had the most bizarre experience outside of a bar yesterday, which my partner very aptly described as "toxic mask-unlinity." (A Thread.)

I was standing outside, waiting for a lyft to get home, when two cis men came out of the bar to smoke cigarettes. They're discussing the masks that folks are wearing in California at the moment because of the very dangerous air pollution from the fires happening.

I happen to be wearing one of those masks, because I think lungs are important and I like breathing. I dunno. One of the men notices me standing there, and says, "Like this f*cker right here. What is this shit?"

He goes on a homophobic rant about me, the pansy with a mask, and what a Very Manly Man he is because he's not afraid to smoke a pack of cigarettes while California is on fire. He doesn't need a mask, because something-something-heterosexual-something.

And part of me is just... impressed by the fact that this dude thinks he's Owning The Gay Libs by not wearing a mask to protect his lungs from smoke and particles in the air that are literally DANGEROUS... while chain-smoking. Outside of a bar.