# Podstawy sieci neuronowych

EN: **Neural network fundamentals** · id `neuralbasics`

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › [Sieci i modele generatywne](https://aiatlasgraph.com/t/neural.md) › Podstawy sieci neuronowych

## Description

- PL: Warstwy, funkcje straty i uczenie reprezentacji.
- EN: Layers, loss functions and representation learning.

## In one sentence

- PL: To podstawowe elementy sieci neuronowej i zasady, dzięki którym uczy się ona na przykładach.
- EN: These are the basic parts of a neural network and the principles that let it learn from examples.

## Problem it solves

- PL: Sieć wygląda jak czarna skrzynka, więc trudno zrozumieć, skąd biorą się jej wyniki i błędy.
- EN: A network looks like a black box, making its results and errors difficult to understand.

## What it does

- PL: Warstwy przekształcają liczby, miara błędu ocenia wynik, a trening wykorzystuje ją do poprawiania połączeń.
- EN: Layers transform numbers, an error measure evaluates the result, and training uses it to adjust connections.

## Everyday example

- PL: Ucząc model rozpoznawać rodzaje kwiatów, obserwujesz, jak kolejne poprawki zmniejszają błędy na przykładach treningowych.
- EN: While training a model to recognise flower types, you watch successive adjustments reduce errors on training examples.

## This concerns you if you…

- PL: zaczynasz poznawać budowę modeli
- PL: chcesz rozumieć, co zmienia się podczas uczenia
- EN: are starting to learn how models are built
- EN: want to understand what changes during training

## Keywords

computation graphs · dense layers · activation functions · gradient propagation · cross-entropy · MSE · normalization layers · residual connections · dropout · weight initialization

## How it works

- PL: Warstwy składają przekształcenia afiniczne z nieliniowościami, tworząc różniczkowalny graf obliczeń. Funkcja straty porównuje wyjście z celem, a optymalizator aktualizuje parametry na podstawie gradientów i przykładów treningowych.
- EN: Layers compose affine transformations with nonlinearities to form a differentiable computation graph. A loss compares outputs with targets, and an optimiser updates parameters using gradients and training examples.

## Limits

- PL: Skala wejść, inicjalizacja i dobór celu wpływają na stabilność uczenia. Duża pojemność sprzyja zapamiętywaniu; niewłaściwy cel może być minimalizowany bez realizacji zamierzonego zadania.
- EN: Input scales, initialisation and objective choice affect learning stability. High capacity enables memorisation; an inappropriate objective can be minimised without achieving the intended task.

## Key terms

- PL: **graf obliczeń**: Zapis operacji i zależności potrzebny do obliczania wyniku oraz pochodnych.
- PL: **bias**: Uczone przesunięcie dodawane do wyniku ważonej sumy wejść.
- EN: **computation graph**: A record of operations and dependencies for calculating outputs and derivatives.
- EN: **bias**: A learned offset added to a weighted sum of inputs.

## Know first

- [Algebra liniowa: wektory i macierze (Linear algebra: vectors and matrices)](https://aiatlasgraph.com/t/linearalgebra.md)
- [Rachunek różniczkowy i gradienty (Calculus and gradients)](https://aiatlasgraph.com/t/calculus.md)

## Examples

- PL: MLP · backpropagation

## Subtopics (7 direct, 7 in total)

- [Funkcje aktywacji (ReLU, GELU, SwiGLU) (Activation functions (ReLU, GELU, SwiGLU))](https://aiatlasgraph.com/t/activations.md): Non-linear functions applied to neuron outputs — without them, a composition of linear layers remains a linear transformation. ReLU is common in vision networks; transformers often use GELU and gated variants such as SwiGLU in feed-forward blocks.
- [Funkcje straty (entropia krzyżowa, MSE) (Loss functions (cross-entropy, MSE))](https://aiatlasgraph.com/t/lossfunctions.md): The number training minimises: how far predictions are from the target. Cross-entropy is used for classification and next-token prediction; mean squared error (MSE) for regression and, among others, noise prediction in diffusion models.
- [Normalizacja warstw (BatchNorm, LayerNorm, RMSNorm) (Normalisation layers (BatchNorm, LayerNorm, RMSNorm))](https://aiatlasgraph.com/t/normalization.md): Layers that rescale activations and often help stabilise deep-network training. BatchNorm uses mini-batch statistics and is typical in CNNs; LayerNorm normalises features within one example and is common in transformers; RMSNorm simplifies LayerNorm and is often used in newer LLMs, usually in a pre-norm layout.
- [Perceptron wielowarstwowy (MLP) (Multilayer perceptron (MLP))](https://aiatlasgraph.com/t/mlp.md): A network of fully connected layers that alternate linear transformations with non-linear activation functions. The MLP is a fundamental neural model and a standard component of feed-forward blocks in transformer architectures.
- [Połączenia resztkowe (skip connections) (Residual (skip) connections)](https://aiatlasgraph.com/t/residual.md): Shortcut connections that add a block's input to its output, allowing the layer to learn a correction relative to the input representation. They enabled effective training of very deep ResNets and are a common component of many modern deep architectures, including transformers.
- [Propagacja wsteczna (backpropagation) (Backpropagation)](https://aiatlasgraph.com/t/backprop.md): An algorithm for computing the gradient of the loss with respect to network parameters: the forward pass computes the output, while the backward pass propagates derivatives through the computation graph using the chain rule. Gradient-based optimisers use these gradients; libraries usually obtain them through automatic differentiation.
- [Regularyzacja sieci: dropout i weight decay (Neural network regularisation: dropout and weight decay)](https://aiatlasgraph.com/t/nnregularization.md): Techniques that curb overfitting in deep networks. Dropout randomly switches off units during training; weight decay penalises large weights; early stopping and data augmentation belong to the same family.

## Related topics

Untyped, symmetric "see also" links.

- [Rachunek różniczkowy i gradienty (Calculus and gradients)](https://aiatlasgraph.com/t/calculus.md)
- [Regresja liniowa i logistyczna (Linear and logistic regression)](https://aiatlasgraph.com/t/linearmodels.md)

## Courses

- [Deep Learning Specialization](<https://www.deeplearning.ai/specializations/deep-learning>)

## Sources

- [Stanford CS336 · Language Modeling from Scratch (2025)](<https://cs336.stanford.edu/spring2025/>) `cs336` · reference: Budowa modeli, dane, trening, systemy, inferencja i post-training.
- [Stanford CS231n · Computer Vision (2026)](<https://cs231n.stanford.edu/>) `vision` · reference: Sieci neuronowe i rozumienie obrazów.
- [Stanford CS229 · Machine Learning (2018, archiwum)](<https://cs229.stanford.edu/syllabus-autumn2018.html>) `cs229` · reference: Klasyczne ML, statystyka i podstawy uczenia. Użyte jako stabilny szkielet, nie przegląd nowości.
- [DeepLearning.AI — course catalogue](<https://www.deeplearning.ai/courses>) `dlai` · course catalogue: 42 topic labels with counts, about 130 courses.

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/neuralbasics · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30