BC MACHINE LEARNING
Saved by @CodyyyGardner

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.
5. Move quickly and professionally. Answer every email / phone call right away, and use well-written and professional communication. This shows that you're proactive, take initiative and can communicate in writing - All great skills for any employee to have.
6. Prepare for the interviews. The first tends to be informal, but have a few questions ready for them. For technical interviews, practice beforehand - and make sure you focus on sharing your thought process and showing your problem solving skills.
7. Negotiate. Never reveal your current salary, and try to make them say a number first. Provide a salary range, with the minimum being what you'd accept. Use other parameters like extra vacation days or WFH opportunities if they can't provide the salary you want.
Thanks for reading! Remember that you're also interviewing the company, so try to stay confident through the entire hiring process.

If this thread was interesting to yo, I go a bit more in-depth on some of these subjects in a blog post here: https://t.co/AASK9YayR0

More from Machine learning

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine learning terms you must know about as a beginner.

🧵👇

These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

(2 / 11)

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

(3 / 11)


In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

(4 / 11)


Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

(5 / 11)
MACHINE LEARNING
Roger Grosse
Roger Grosse
@RogerGrosse
Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ


The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.
MACHINE LEARNING
Advertisement
Unknown \U0001f464
Unknown 👤
@console_404
10 PYTHON 🐍 libraries for machine learning.

Retweets are appreciated.
[ Thread ]


1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN


2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo


3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx


4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4
MACHINE LEARNING
Cassie Kozyrkov
Cassie Kozyrkov
@quaesita
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:
MACHINE LEARNING
Santiago
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)
MACHINE LEARNING , TENSOR BASICS

You May Also Like

Aditya Todmal
Aditya Todmal
@AdityaTodmal
A Thread on the Boss himself @Mitesh_Engr

Mitesh Sir's Positional Option Selling 101:

• How to find direction
• Which options to sell
• How to deploy capital
• Exit criteria
• What ROI he targets weekly
• What % risk he takes

Done with the help of @niki_poojary


How @Mitesh_Engr Sir finds the direction?

• Daily charts S/R
• 75 min charts S/R
• Intraday trend
• Always play directional
• Never trades in strategies


Which options to sell in weekly expiry according to @Mitesh_Engr?

• Weekly candle High/Low
• Sell 1% away options from those
• Exit when levels breached


Position Sizing by @Mitesh_Engr Sir.

How to deploy your capital?

• First sell 20%
• Pyramid the next day
• When to exit
• What to do when view goes wrong
• What to do with idle capital


Some important tweets of @Mitesh_Engr Sir

• What Data to look at
• ROI for safe players per month
• Max risk per day
• Gather knowledge and play directional
• Never play strategies
OPTIONS LEARNING , ALL , MPLEARNINGS
Anshul Pandey
Anshul Pandey
@Anshulspiritual
TYPES OF SINS(PAAP) AS PER SKAND PURAN.

There are three types of Adharma- Sthool, Sukshma and Ati Sukshma. The group of Sins in the Sthul section carries you to Narak. These sins are committed through your thoughts, words and deeds.


It is of four types.

1)First is the sin committed by your mental state-
Thinking about woman other then your spouse.

A)Resolution to amass wealth from others.

B)Thinking ill about others.

C)Thinking of doing a forbidden or unworthy deed.

2)Second is the sin that you commit through your words :-

A)Talking irrelevant things.

B)Telling a lie.

C)Speaking harshly.

D)Talking ill about others.

3)Third type of sin is committed through your body actions-

A)Eating food not prescribed to you or forbidden.

B)Violence.

C)Eating fancier food.

D)Acquiring other's wealth.

So these twelve types of sins.

There are six types of sinners or Mahapatak

1)One not bowing his head in front of a temple.

2)Never praise Shiva.

3)People who behave untowardly way in front of Ishwar.

4)People who do not show respect to Ishwar, guru or one who are undignified.

5)One who do not behave properly.

6)One who is jealous of other bhakts.

Below is the list of sins which people may commit.

1)One who does not help a needy person or a person in pain,
RELIGION
Advertisement
Nicolas Cole
Nicolas Cole
@Nicolascole77
🚢 Atomic Essays Republishing Framework 🚢

1. Write Atomic Essay
2. Post image on Twitter
3. (Bonus) Copy/paste text as thread
4. Find relevant Question on Quora. Copy/paste Atomic Essay + image.
5. Copy/paste again on Medium.
6. Again on LinkedIn
7. Win

Guide below ✍️🚢👇

Step 1: Write Atomic Essay

I really enjoy writing right inside the Figma template. It helps give me a good sense of exactly how much "real estate" I have/have left before my time us up and I'm out of space.

Once finished, I export the image and send to my phone on Slack.


Step 2: Post image on Twitter

Before I publish my Atomic Essay on Twitter, I use the Edit/Photo Markup function on the iPhone to highlight standout sentences.

These are usually power-phrases: things the reader skims and thinks, "That's interesting," prompting them to read more.


Step 3: For the Ship 30 for 30 challenge, I only turn Atomic Essays into threads if...

1. The piece is SUPER actionable and lends itself well to the thread format (lots of bullets, quick points, lists, etc.)

or...

2. I'm referencing research and want to link to the pieces.

Step 4: Find relevant Question on Quora. Copy/paste Atomic Essay + image.

Search on Quora around the topic you wrote about. Find a related Question. Copy/paste the whole Atomic Essay as your "answer" (& fix formatting). Then include image so it appears in the thumbnail.
WRITING
Groww
Groww
@_groww
You want to buy a house.

You take a loan from the bank.

The bank asks you, “what if you don’t pay back?”

You say, “take my home and sell it”.

(Thread)

That makes sense. The bank gives you a loan and in case you don’t pay, the bank will take over your house and sell it and recover its money.

That’s collateral.

Banks also lend money to big corporations. With them, banks don’t always have collateral.

So a loan where the bank has collateral is considered safer for the bank.

In the early 2000s, the banks in the USA were giving out loans to people for buying houses.

The economy had just recovered from the tech bubble in 2000 where many banks had given out loans to businesses that had shut down.

In that backdrop, the banks found giving home loans safer.
ALL , 2008 CRASH , 2008 RECESSION
Matt Fuller
Matt Fuller
@MEPFuller
They’re shooting into the chamber.
POLITICS