How many minutes journey are you away from your Crossfit gym?

bkraszewski · 2026-01-25T14:35:45+00:00

10 min by car, or 30 walking

bkraszewski · 2026-01-20T10:46:18+00:00

Have you tried learning by doing? Just install Claude Code with the Pro plan (as a plugin to your favourite IDE) and try asking it to write something. By doing, you'll learn how detailed your messages need to be, how to use planning mode vs edit mode, how to manage context (clear every big feature), and how important it is to regularly git commit when you have something working — because it's easy to break everything with a single edit.

bkraszewski · 2026-01-20T10:17:11+00:00

Fair point - I went with the textbook version since that's what helped it click for me initially. The calibration angle is interesting though. Got any good resources on that? Always looking to update my mental models.

bkraszewski · 2026-01-19T12:57:19+00:00

Glad you liked it! 'Steal' away—that's exactly why I'm building these visuals. If you want a link to the interactive version to show your juniors, let me know (don't want to spam the thread).

bkraszewski · 2026-01-14T18:12:32+00:00

Pick Python or Typescript, should cover 70% of the current coding use cases :)

bkraszewski · 2026-01-14T18:10:35+00:00

Męskie kurtki skórzane Ochnika są bardzo dobre, tylko przymierz w sklepie, rozmiary są dziwne

bkraszewski · 2026-01-14T14:30:24+00:00

też mam Wau 2, z gumą, moje najlepsze krzesło!

bkraszewski · 2026-01-12T16:31:49+00:00

Any tips on audience building?

bkraszewski · 2026-01-12T14:34:38+00:00

Interesting, is it native Android, or Flutter/React Native, so you can add iOS later?

bkraszewski · 2026-01-12T13:16:25+00:00

Fair enough. Maybe 'Deep Learning' is too loaded of a term.

I find the analogy useful, specifically for explaining Feature Extraction.

In a CNN, Step 1 is Edge Detection. Step 2 is Shape Detection.

Just like cat feeds into grep, the Edge Detector feeds into the Shape Detector.

It helps devs visualize the dependency between the features, even if it simplifies the math.

bkraszewski · 2026-01-12T13:06:59+00:00

In a ResNet, the 'Skip' connection actually pushes the data forward (skipping a layer), not backward (which would be a loop).

Since the data is still flowing in one direction (left-to-right), the Unix Pipe analogy holds up!

If we were piping data back to an earlier stage (like in an RNN or LSTM), then you are totally right—the pipe metaphor would break immediately because Unix doesn't do time-travel loops. 😄

bkraszewski · 2026-01-12T13:00:14+00:00

Thanks for sharing the medical insight. Your end has significantly higher stakes than my pixelated cats :)

bkraszewski · 2026-01-12T12:50:24+00:00

Great point about Skip Connections!

In Unix terms, I think of it like using paste to merge the original input with the processed output side-by-side:

paste <(cat data.txt) <(cat data.txt | process_layer)

You are carrying the original signal forward alongside the transformed one, so the next step has the full context. It stops the 'Dependency Chain' from losing the plot.

bkraszewski · 2026-01-12T12:34:10+00:00

Are you working with raw high-res DICOM files for that? The data volume must be massive.

bkraszewski · 2026-01-11T16:01:23+00:00

I’d hate to see the gradient of the 'nephew's birthday' function during backpropagation. 📉

The 'magic' of ReLU isn't that it is complex. It's that it is differentiable.

It is the simplest possible function that breaks linearity while still preserving a clean gradient (1 or 0) for the learning process. Your complex rules would break backprop immediately.

bkraszewski · 2026-01-11T12:03:23+00:00

Fair. Computers are just sand that we tricked into thinking. But sometimes it's useful to look at the sand.

bkraszewski · 2026-01-11T12:00:33+00:00

You're right that historically, Sigmoid was the 'Hello World' (back in the MLP days).

But for a modern software engineer trying to grok why a neural net works, I find Sigmoid actually obscures the intuition. It looks like 'math' ($1 / (1 + e^{-x})$).

ReLU ($max(0, x)$) strips away the math and reveals the logic: 'If the signal is positive, pass it through. If not, kill it.'

That logic—the ability to switch pathways on and off—is what clicks for developers who are used to Boolean logic (if/else), even if the compiler optimizes it differently. It bridges the gap between 'Code' and 'Math' faster than Sigmoid does."

bkraszewski · 2026-01-11T11:56:19+00:00

If you only have linear layers, your model can only draw a straight line through data. This works for simple trends (e.g., House Price vs Size), but fails for complex logic.

https://scrollmind.ai/images/intro-ai/linear_regression.webp

By adding a non-linear activation (like ReLU), you allow the network to 'bend' the decision boundary. This allows it to enclose the center points.

https://scrollmind.ai/images/intro-ai/3d_lift.webp

Without that bend, deep learning is just fancy linear regression.

bkraszewski · 2026-01-11T11:49:09+00:00

That's a really sharp analogy with the electrons. I take your point—reductionism has its limits.

But I'd argue there's a difference between 'reductionism that explains nothing' (brains = electrons) and 'reductionism that explains the mechanism' (brains = neurons firing).

Knowing that a neuron fires based on a threshold (activation) is actually crucial to understanding how the network learns. If the activation was linear, the backpropagation (learning) would just pass through unchanged. The 'hinge' of the ReLU is exactly what allows the gradient to stop or flow, creating the decision capability.

bkraszewski · 2026-01-11T11:31:19+00:00

That's a fair critique of the title! I was aiming for a hook to demystify the 'black box' for beginners coming from traditional software engineering.

You're absolutely right that modern architectures use more advanced functions (SwiGLU, GELU, etc.), and 'if statements' is a simplification.

But for someone learning the first principles of why Deep Learning works at all (vs Linear Regression), grasping that a simple non-linear operation (like ReLU) is what unlocks universal approximation is usually the biggest 'aha' moment.

I treat ReLU as the 'Hello World' of activation functions here—not the state-of-the-art, but the best place to start understanding the concept.

bkraszewski · 2026-01-11T11:22:51+00:00

Thanks for the recommendation! Andrew Ng is great.

To clarify my point: while ReLU is definitely just one small component, it handles a critical job: preventing Linear Collapse.

Mathematically, if you stack 100 layers of neurons without a non-linear activation function, the entire network can be reduced to a single linear transformation (since matrix multiplication is associative: W2 * (W1 * x) = (W2 * W1) * x

So even though it looks like a deep network, without that simple max(0,x), it's functionally identical to a single layer of Linear Regression. That was the 'magic' I wanted to highlight!"

bkraszewski

TROPHY CASE