11 key concepts of Machine Learning.

— Supervised Learning Edition —

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I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.

One of the most popular activation functions used in deep learning models is ReLU.

I asked: "Is ReLU continuous and differentiable?"

Surprisingly, a lot of people were confused about this.

Let's break this down step by step: ↓

Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.

If you draw this function, you'll get the attached chart.

Notice there are no discontinuities in the function.

This should be enough to answer half of the original question: the ReLU function is continuous.

Let's now think about the differentiable part.

A necessary condition for a function to be differentiable: it must be continuous.

ReLU is continuous. That's good, but not enough.

Its derivative should also exist for every individual point.

Here is where things get interesting.

We can compute the derivative of a function using the attached formula.

(I'm not going to explain where this is coming from; you can trust me on this one.)

We can use this formula to see whether ReLU is differentiable.

Some of the things I've learned in more than 20 years in the tech industry.

You need to hear these.

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Listen to more people who don't look like you, don't speak your language, and don't come from the same place you do.

We aren't doing this near enough.

Small habits compound.

No small improvement is too small.

Just aim for something new every day, and you'll be surprised at the end.

Take responsibility.

It doesn't really matter how you feel, I'm sure you could have done better.

Always keep the big picture in mind and never lose the forest for the trees.

Be the person that pulls everyone out of the rabbit holes.

A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
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For this exercise, you'll need :
- A computer/phone
- An internet connection
- Very basic Python knowledge
- Willingness to learn

What you do not need:
- Complex math
- An expensive computer
- A PhD

Math will come later on in your machine learning journey :)

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Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

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We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

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Here's the code👇

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Okay, doing my first baby steps with r2frida (which combines the power of @radareorg and @fridadotre).

Gonna share my progress in this thread (live, so keep calm).

The goal: Runtime inspection of data sent out by TikTok !!before!! it gets encrypted

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First of all, we do not start from zero. I got some prior knowledge from past reversing attempts and want to share some important facts.

TikTok's (log data) encryption is accomplished by a native library. The Android Java code just serves as proxy function to the native function

The decompiled code for the respective native JNI function of an older TikTok version looks something like this, but in this example I use the most current TT version (no statical analysis done, yet)

In case you never reversed native libraries which were build to interface with Android Java layer via JNI, I highly suggest the entry level introduction on the topic by @maddiestone

Before we start, I want to pinpoint some important aspects (which are also covered by Maddie's videos).

1) Unlike raw C-functions, JNI functions like the one showcased above, receive pointers to complex Java objects .

F.e. a function receiving a String on the Java layer...

An introduction to one of the the most basic structures used in machine learning: a tensor.

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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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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.

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#,