{"id":44121,"date":"2026-06-16T00:51:16","date_gmt":"2026-06-15T22:51:16","guid":{"rendered":"https:\/\/www.derivaty.sk\/?p=44121"},"modified":"2026-01-05T14:03:11","modified_gmt":"2026-01-05T13:03:11","slug":"deep-learning-hlboke-neuronove-siete-a-ich-prakticke-vyuzitie","status":"publish","type":"post","link":"https:\/\/www.autoskoly.sk\/news\/deep-learning-hlboke-neuronove-siete-a-ich-prakticke-vyuzitie\/","title":{"rendered":"Deep Learning: Hlbok\u00e9 neur\u00f3nov\u00e9 siete a ich praktick\u00e9 vyu\u017eitie"},"content":{"rendered":"<h2>Co je hlubok\u00e9 u\u010den\u00ed (Deep Learning) a pro\u010d na n\u011bm z\u00e1le\u017e\u00ed<\/h2>\n<p>Hlubok\u00e9 u\u010den\u00ed je podmno\u017eina strojov\u00e9ho u\u010den\u00ed zalo\u017een\u00e1 na <em>v\u00edcevrstv\u00fdch neuronov\u00fdch s\u00edt\u00edch<\/em>, kter\u00e9 se u\u010d\u00ed reprezentace dat v hierarchi\u00edch od jednoduch\u00fdch k st\u00e1le abstraktn\u011bj\u0161\u00edm. D\u00edky t\u00e9to vlastnosti dok\u00e1\u017ee DL <strong>automaticky extrahovat p\u0159\u00edznaky<\/strong> z obrazu, textu, zvuku i tabulkov\u00fdch dat a dosahovat \u0161pi\u010dkov\u00fdch v\u00fdsledk\u016f v rozpozn\u00e1v\u00e1n\u00ed obrazu, porozum\u011bn\u00ed p\u0159irozen\u00e9mu jazyku, generativn\u00edm \u00faloh\u00e1ch i \u0159\u00edzen\u00ed dynamick\u00fdch syst\u00e9m\u016f.<\/p>\n<h2>Matematick\u00e9 z\u00e1klady: funkce ztr\u00e1ty, gradienty, optimalizace<\/h2>\n<ul>\n<li><strong>Ztr\u00e1tov\u00e1 funkce (loss)<\/strong>: m\u011b\u0159\u00ed nesoulad mezi predikc\u00ed a c\u00edlem (nap\u0159. k\u0159\u00ed\u017eov\u00e1 entropie, MSE). Volba lossu determinuje chov\u00e1n\u00ed u\u010den\u00ed.<\/li>\n<li><strong>Backpropagation<\/strong>: efektivn\u00ed v\u00fdpo\u010det gradient\u016f pomoc\u00ed \u0159et\u011bzov\u00e9ho pravidla p\u0159es vrstvy s\u00edt\u011b.<\/li>\n<li><strong>Optimaliz\u00e1tory<\/strong>: SGD s momentum, Adam, AdamW, RMSProp \u2013 li\u0161\u00ed se adaptivitou krok\u016f, pam\u011bt\u00ed gradient\u016f a regularizac\u00ed.<\/li>\n<li><strong>Normalizace<\/strong>: batch\/layer\/group norm stabilizuj\u00ed distribuci aktivac\u00ed a zrychluj\u00ed tr\u00e9nink.<\/li>\n<li><strong>Aktiva\u010dn\u00ed funkce<\/strong>: ReLU, GELU, SiLU\/Swish, softmax; ovliv\u0148uj\u00ed nelinearitu a gradientn\u00ed tok.<\/li>\n<\/ul>\n<h2>Z\u00e1kladn\u00ed architektury neuronov\u00fdch s\u00edt\u00ed<\/h2>\n<ul>\n<li><strong>V\u00edcevrstv\u00e9 perceptrony (MLP)<\/strong>: pln\u011b propojen\u00e9 vrstvy pro tabulkov\u00e1 data a jednodu\u0161\u0161\u00ed predikce.<\/li>\n<li><strong>Konvolu\u010dn\u00ed s\u00edt\u011b (CNN)<\/strong>: lok\u00e1ln\u00ed receptivn\u00ed pole a sd\u00edlen\u00ed vah pro obraz, video, sign\u00e1ly.<\/li>\n<li><strong>Rekurentn\u00ed s\u00edt\u011b (RNN, LSTM, GRU)<\/strong>: zpracov\u00e1n\u00ed sekvenc\u00ed se stavovou pam\u011bt\u00ed; vhodn\u00e9 pro \u010dasov\u00e9 \u0159ady, \u0159e\u010d.<\/li>\n<li><strong>Transformery<\/strong>: mechanismus pozornosti (self-attention) pro paraleln\u00ed u\u010den\u00ed dlouh\u00fdch z\u00e1vislost\u00ed v textu, obrazu i multimod\u00e1ln\u00edch datech.<\/li>\n<li><strong>Autoenkod\u00e9ry a variace<\/strong>: komprese a generov\u00e1n\u00ed reprezentac\u00ed (denoising, variational AE).<\/li>\n<li><strong>Generativn\u00ed modely<\/strong>: GAN, normalizing flows, difuzn\u00ed modely pro synt\u00e9zu obraz\u016f, zvuku a textu.<\/li>\n<\/ul>\n<h2>Datov\u00e9 pipeline: od sb\u011bru po feature store<\/h2>\n<ol>\n<li><strong>Sb\u011br a kur\u00e1torstv\u00ed<\/strong>: reprezentativnost, vyv\u00e1\u017eenost t\u0159\u00edd, pr\u00e1vn\u00ed a etick\u00e9 aspekty (licence, souhlasy).<\/li>\n<li><strong>P\u0159edzpracov\u00e1n\u00ed<\/strong>: \u010di\u0161t\u011bn\u00ed, deduplikace, tokenizace\/segmentace, normalizace, augmentace (obraz, audio, text).<\/li>\n<li><strong>Rozd\u011blen\u00ed<\/strong>: train\/validation\/test (\u010dasto 70\/15\/15), p\u0159\u00edpadn\u011b \u010dasov\u011b konzistentn\u00ed split pro time-series.<\/li>\n<li><strong>Verzov\u00e1n\u00ed<\/strong>: data, \u0161t\u00edtky, k\u00f3d i konfigurace mus\u00ed b\u00fdt verzov\u00e1ny (DVC, Git, MLflow artefakty).<\/li>\n<li><strong>Feature store<\/strong>: sd\u00edlen\u00e9 a konzistentn\u00ed rysy nap\u0159\u00ed\u010d tr\u00e9ninkem a inferenc\u00ed minimalizuj\u00ed tr\u00e9nink\u2013serving skew.<\/li>\n<\/ol>\n<h2>Tr\u00e9ninkov\u00e9 strategie a \u0161k\u00e1lov\u00e1n\u00ed<\/h2>\n<ul>\n<li><strong>Mini-batch u\u010den\u00ed<\/strong> s m\u00edch\u00e1n\u00edm (shuffling) a schedulery u\u010den\u00ed (cosine, step, warmup).<\/li>\n<li><strong>Regularizace<\/strong>: dropout, weight decay, data augmentation, early stopping.<\/li>\n<li><strong>P\u0159enesen\u00e9 u\u010den\u00ed (transfer learning)<\/strong>: fine-tuning p\u0159edtr\u00e9novan\u00fdch model\u016f; \u0161et\u0159\u00ed data i v\u00fdpo\u010det.<\/li>\n<li><strong>Paralelizace<\/strong>: data\/model\/tensor\/pipeline parallelism; mixed precision (FP16\/BF16) a checkpointing pro pam\u011b\u0165.<\/li>\n<li><strong>Curriculum a active learning<\/strong>: \u0159\u00edzen\u00e9 po\u0159ad\u00ed vzork\u016f a c\u00edlen\u00e9 do\u0161t\u00edtkov\u00e1n\u00ed nejist\u00fdch p\u0159\u00edklad\u016f.<\/li>\n<\/ul>\n<h2>Hodnocen\u00ed modelu: metriky, validace a odolnost<\/h2>\n<ul>\n<li><strong>Metriky<\/strong>: p\u0159esnost, F1, ROC-AUC, mAP, BLEU\/ROUGE, WER, NDCG \u2013 volba z\u00e1vis\u00ed na dom\u00e9n\u011b.<\/li>\n<li><strong>Validace<\/strong>: k\u0159\u00ed\u017eov\u00e1 validace pro men\u0161\u00ed datasety, \u010dasov\u011b citliv\u00e9 splitov\u00e1n\u00ed pro sekvence.<\/li>\n<li><strong>Kalibrace pravd\u011bpodobnost\u00ed<\/strong>: Platt scaling, isotonic regression \u2013 d\u016fle\u017eit\u00e9 pro rozhodov\u00e1n\u00ed s rizikem.<\/li>\n<li><strong>Robustnost<\/strong>: testy na out-of-distribution vzorc\u00edch, \u0161um, adversari\u00e1ln\u00ed perturbace.<\/li>\n<li><strong>Spolehlivost v provozu<\/strong>: monitoring datov\u00fdch drift\u016f, <em>shadow deployments<\/em>, A\/B a canary testy.<\/li>\n<\/ul>\n<h2>Interpretovatelnost a vysv\u011btlitelnost<\/h2>\n<ul>\n<li><strong>Glob\u00e1ln\u00ed vs. lok\u00e1ln\u00ed<\/strong>: v\u00fdznam rys\u016f v pr\u016fm\u011bru vs. pro konkr\u00e9tn\u00ed predikci.<\/li>\n<li><strong>Post-hoc metody<\/strong>: SHAP, LIME, saliency\/gradient mapy, attention vizualizace.<\/li>\n<li><strong>Vnit\u0159n\u00ed interpretace<\/strong>: sparsity, monotonicity, konceptov\u00e9 aktivace, prototypov\u00e9 s\u00edt\u011b.<\/li>\n<li><strong>Regulovan\u00e9 dom\u00e9ny<\/strong>: vysv\u011btlitelnost je nezbytn\u00e1 ve financ\u00edch, zdravotnictv\u00ed a ve\u0159ejn\u00e9m sektoru.<\/li>\n<\/ul>\n<h2>Bezpe\u010dnost, etika a governance<\/h2>\n<ul>\n<li><strong>Bias a f\u00e9rovost<\/strong>: audit dataset\u016f, metriky parity (demographic parity, equalized odds), mitigace reweighingem a adversari\u00e1ln\u00edmi penalizacemi.<\/li>\n<li><strong>Soukrom\u00ed<\/strong>: federovan\u00e9 u\u010den\u00ed, diferenci\u00e1ln\u00ed soukrom\u00ed, syntetick\u00e1 data a bezpe\u010dn\u00e1 agregace gradient\u016f.<\/li>\n<li><strong>Adversari\u00e1ln\u00ed hrozby<\/strong>: poisoning, evasion, model stealing; obrany jako adversarial training a detekce anom\u00e1li\u00ed.<\/li>\n<li><strong>Model governance<\/strong>: schvalovac\u00ed workflow, evidence verz\u00ed, datov\u00fdch zdroj\u016f, rizik a odpov\u011bdnost\u00ed.<\/li>\n<\/ul>\n<h2>Nasazen\u00ed (MLOps): od tr\u00e9ningu k produkci<\/h2>\n<ol>\n<li><strong>Bal\u00ed\u010dkov\u00e1n\u00ed<\/strong>: export do ONNX\/TorchScript, kvantizace a pruning pro latenci a footprint.<\/li>\n<li><strong>Serving<\/strong>: REST\/gRPC mikroservisy, batch\/offline inference, stream inference (Kafka, Flink).<\/li>\n<li><strong>Observabilita<\/strong>: metriky latence a propustnosti, business metriky, drift detekce a zp\u011btn\u00e1 smy\u010dka do tr\u00e9ninku.<\/li>\n<li><strong>CI\/CD pro ML<\/strong>: automatizace test\u016f, datov\u00e9 validace (schema, statistiky), promotion artefakt\u016f p\u0159es prost\u0159ed\u00ed.<\/li>\n<\/ol>\n<h2>Hardwarov\u00e1 a softwarov\u00e1 ekosyst\u00e9mov\u00e1 vrstva<\/h2>\n<ul>\n<li><strong>Akceler\u00e1tory<\/strong>: GPU (CUDA), specializovan\u00e9 \u010dipy (TPU, NPU). D\u016fraz na pam\u011b\u0165ovou propustnost a paralelismus.<\/li>\n<li><strong>R\u00e1mce<\/strong>: PyTorch a TensorFlow jako de facto standardy; JAX pro funkcion\u00e1ln\u00ed, kompilovan\u00e9 workflow.<\/li>\n<li><strong>Distribuovan\u00e9 knihovny<\/strong>: Horovod, PyTorch Distributed, DeepSpeed; orchestrace p\u0159es Kubernetes.<\/li>\n<\/ul>\n<h2>Typick\u00e9 aplika\u010dn\u00ed oblasti<\/h2>\n<ul>\n<li><strong>Po\u010d\u00edta\u010dov\u00e9 vid\u011bn\u00ed<\/strong>: klasifikace, detekce (anchor-free\/anchor-based), segmentace (U-Net), OCR.<\/li>\n<li><strong>NLP<\/strong>: jazykov\u00e9 modely, strojov\u00fd p\u0159eklad, sumarizace, hled\u00e1n\u00ed s re-rankingem, extrakce znalost\u00ed.<\/li>\n<li><strong>Audio a \u0159e\u010d<\/strong>: ASR\/TTS, identifikace mluv\u010d\u00edho, hudebn\u00ed doporu\u010dov\u00e1n\u00ed.<\/li>\n<li><strong>Doporu\u010dovac\u00ed syst\u00e9my<\/strong>: vektorizace u\u017eivatel\u016f a polo\u017eek, sekven\u010dn\u00ed doporu\u010dov\u00e1n\u00ed, hybridn\u00ed modely.<\/li>\n<li><strong>\u010casov\u00e9 \u0159ady a IoT<\/strong>: progn\u00f3zy, detekce anom\u00e1li\u00ed, prediktivn\u00ed \u00fadr\u017eba v telekomunikac\u00edch a pr\u016fmyslu.<\/li>\n<li><strong>Kyberbezpe\u010dnost<\/strong>: detekce malwaru, phishingu a anom\u00e1ln\u00edho chov\u00e1n\u00ed v s\u00edti.<\/li>\n<\/ul>\n<h2>Vzory tr\u00e9ninku pro praxi: od mal\u00fdch dat po foundation modely<\/h2>\n<ul>\n<li><strong>Low-data sc\u00e9n\u00e1\u0159e<\/strong>: transfer learning, few-shot a prompt-based p\u0159izp\u016fsoben\u00ed velk\u00fdch model\u016f.<\/li>\n<li><strong>St\u0159edn\u00ed data<\/strong>: siln\u00e1 augmentace, self-supervised pretraining (contrastive learning), semi-supervised p\u0159\u00edstup.<\/li>\n<li><strong>Velk\u00e1 data<\/strong>: \u0161k\u00e1lovan\u00e9 tr\u00e9ninky, curriculum, deduplikace a datov\u00e1 hygienick\u00e1 pravidla.<\/li>\n<li><strong>Foundation a multimod\u00e1ln\u00ed modely<\/strong>: jednotn\u00e1 reprezentace text\u2013obraz\u2013audio a p\u0159izp\u016fsoben\u00ed na dom\u00e9nov\u00e9 \u00falohy.<\/li>\n<\/ul>\n<h2>Generativn\u00ed AI: principy a bezpe\u010dn\u00e9 vyu\u017eit\u00ed<\/h2>\n<ul>\n<li><strong>Difuzn\u00ed modely<\/strong>: postupn\u00e1 denoizace generuj\u00edc\u00ed vysoce kvalitn\u00ed obraz a audio.<\/li>\n<li><strong>Jazykov\u00e9 modely<\/strong>: autoregresivn\u00ed predikce token\u016f, \u0159et\u011bzen\u00ed n\u00e1stroj\u016f a retrieval-augmented generation pro pr\u00e1ci s podnikov\u00fdmi daty.<\/li>\n<li><strong>Kontrola a guardrails<\/strong>: filtry obsahu, detekce citliv\u00fdch informac\u00ed, audit prompt\u016f a v\u00fdstup\u016f.<\/li>\n<\/ul>\n<h2>Metodiky lad\u011bn\u00ed a \u0159\u00edzen\u00ed experiment\u016f<\/h2>\n<ul>\n<li><strong>Hyperparametrick\u00e9 vyhled\u00e1v\u00e1n\u00ed<\/strong>: grid\/random, bayesovsk\u00e1 optimalizace, multi-fidelity metody (ASHA, Hyperband).<\/li>\n<li><strong>Experiment tracking<\/strong>: MLflow, Weights &amp; Biases \u2013 metriky, konfigurace, artefakty a porovn\u00e1v\u00e1n\u00ed b\u011bh\u016f.<\/li>\n<li><strong>Reprodukovatelnost<\/strong>: seedov\u00e1n\u00ed, determinismus, deklarativn\u00ed konfigurace a zamyk\u00e1n\u00ed verz\u00ed z\u00e1vislost\u00ed.<\/li>\n<\/ul>\n<h2>Edge AI a real-time inferov\u00e1n\u00ed<\/h2>\n<ul>\n<li><strong>Kompaktn\u00ed modely<\/strong>: kvantizace (INT8), pruning, znalostn\u00ed destilace pro embedded za\u0159\u00edzen\u00ed a mobil.<\/li>\n<li><strong>On-device soukrom\u00ed<\/strong>: citliv\u00e9 sign\u00e1ly z\u016fst\u00e1vaj\u00ed na za\u0159\u00edzen\u00ed; federovan\u00e9 aktualizace modelu.<\/li>\n<li><strong>Latency-first n\u00e1vrh<\/strong>: c\u00edlen\u00ed na SLA (p99), batching, asynchronn\u00ed fronty a cache v\u00fdsledk\u016f.<\/li>\n<\/ul>\n<h2>Integrace do podnikov\u00fdch syst\u00e9m\u016f a datov\u00e9 infrastruktury<\/h2>\n<ul>\n<li><strong>Data lakehouse<\/strong>: jednotn\u00e9 \u00falo\u017ei\u0161t\u011b pro tr\u00e9nink i analytiku; schemata a kvalita dat jako kontrakty.<\/li>\n<li><strong>Retrieval<\/strong>: vektorov\u00e9 datab\u00e1ze pro vyhled\u00e1v\u00e1n\u00ed v embeddingov\u00fdch prostorech (semantick\u00fd search, RAG).<\/li>\n<li><strong>Bezpe\u010dnost a compliance<\/strong>: \u0159\u00edzen\u00ed p\u0159\u00edstupu, anonymizace, auditn\u00ed z\u00e1znamy a \u0159\u00edzen\u00ed reten\u010dn\u00ed politiky.<\/li>\n<\/ul>\n<h2>Praktick\u00e9 checklisty pro projekt hlubok\u00e9ho u\u010den\u00ed<\/h2>\n<ul>\n<li><strong>Definice probl\u00e9mu<\/strong>: c\u00edlov\u00e1 metrika, byznysov\u00fd dopad, omezen\u00ed latence a n\u00e1klad\u016f.<\/li>\n<li><strong>Data<\/strong>: reprezentativnost, licence, stratifikace split\u016f, verzov\u00e1n\u00ed a kvalita \u0161t\u00edtk\u016f.<\/li>\n<li><strong>Tr\u00e9nink<\/strong>: baseline model, scheduler, early stopping, monitoring overfittingu.<\/li>\n<li><strong>Hodnocen\u00ed<\/strong>: robustnost, OOD testy, fairness metriky a kalibrace.<\/li>\n<li><strong>Nasazen\u00ed<\/strong>: SLO, autoscaling, fallback strategie, observabilita a incident response.<\/li>\n<li><strong>Governance<\/strong>: dokumentace modelu (model card), risk assessment, schvalov\u00e1n\u00ed a revize.<\/li>\n<\/ul>\n<h2>Limity hlubok\u00e9ho u\u010den\u00ed a kdy zvolit jin\u00fd p\u0159\u00edstup<\/h2>\n<ul>\n<li><strong>Nedostatek dat<\/strong>: preferujte jednodu\u0161\u0161\u00ed modely, silnou pravidelnou validaci a dom\u00e9nov\u00e9 rysy.<\/li>\n<li><strong>Vysok\u00e9 n\u00e1roky na vysv\u011btlitelnost<\/strong>: rozhodovac\u00ed stromy, line\u00e1rn\u00ed modely nebo hybridy s interpretovateln\u00fdmi vrstvami.<\/li>\n<li><strong>Striktn\u00ed latency a omezen\u00fd hardware<\/strong>: klasick\u00e9 ML, destilace \u010di ru\u010dn\u011b navr\u017een\u00e9 p\u0159\u00edznaky mohou b\u00fdt efektivn\u011bj\u0161\u00ed.<\/li>\n<\/ul>\n<h2>Budouc\u00ed sm\u011bry a trendy<\/h2>\n<ul>\n<li><strong>Multimod\u00e1ln\u00ed syst\u00e9my<\/strong> sjednocuj\u00edc\u00ed text, obraz, \u0159e\u010d a akce do jednotn\u00e9 architektury.<\/li>\n<li><strong>U\u010den\u00ed s men\u0161\u00ed superviz\u00ed<\/strong>: self-supervised, weakly-supervised a syntetick\u00e1 data.<\/li>\n<li><strong>Energetick\u00e1 efektivita<\/strong>: zelen\u00e9 AI, optimalizace tr\u00e9ninku i inferenc\u00ed z hlediska CO<sub>2<\/sub>.<\/li>\n<li><strong>Bezpe\u010dn\u00e9 a spolehliv\u00e9 AI<\/strong>: form\u00e1ln\u00ed verifikace vlastnost\u00ed model\u016f, odolnost v\u016f\u010di \u00fatok\u016fm a \u0159\u00edzen\u00ed rizik.<\/li>\n<\/ul>\n<h2>Z\u00e1v\u011br<\/h2>\n<p>Hlubok\u00e9 u\u010den\u00ed p\u0159edstavuje univerz\u00e1ln\u00ed r\u00e1mec pro u\u010den\u00ed reprezentac\u00ed, kter\u00fd dok\u00e1\u017ee \u0161k\u00e1lovat s daty i v\u00fdpo\u010detn\u00edm v\u00fdkonem a propojuje statistiku, optimalizaci, softwarov\u00e9 in\u017een\u00fdrstv\u00ed a dom\u00e9novou expert\u00edzu. \u00dasp\u011bch v praxi vy\u017eaduje nejen siln\u00e9 modely, ale tak\u00e9 kvalitn\u00ed data, robustn\u00ed MLOps, etick\u00e9 standardy a pr\u016fb\u011b\u017en\u00fd monitoring. Organizace, kter\u00e9 tyto pil\u00ed\u0159e zvl\u00e1dnou, prom\u011bn\u00ed DL v <strong>konkuren\u010dn\u00ed v\u00fdhodu<\/strong> nap\u0159\u00ed\u010d IT, telekomunikacemi, webem i datovou analytikou.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep learning vysvetlen\u00fd prakticky: architekt\u00fary, tr\u00e9ning na GPU a regulariz\u00e1cia. Kedy sa hod\u00ed a ako zvl\u00e1dnu\u0165 d\u00e1ta, v\u00fdkon aj inferenciu v produkcii.<\/p>\n","protected":false},"author":46,"featured_media":84121,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[617],"tags":[1641,1642,1643,1644,1645,1646,1647,63],"class_list":["post-44121","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-telekomunikacie","tag-cnn","tag-deep-learning","tag-gpu","tag-neuronove-siete","tag-regularizacia","tag-rnn","tag-transformer","tag-trening"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Deep Learning: Hlbok\u00e9 neur\u00f3nov\u00e9 siete a ich praktick\u00e9 vyu\u017eitie - Auto\u0161koly.sk<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.autoskoly.sk\/news\/deep-learning-hlboke-neuronove-siete-a-ich-prakticke-vyuzitie\/\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning: Hlbok\u00e9 neur\u00f3nov\u00e9 siete a ich praktick\u00e9 vyu\u017eitie - Auto\u0161koly.sk\" \/>\n<meta property=\"og:description\" content=\"Deep learning vysvetlen\u00fd prakticky: architekt\u00fary, tr\u00e9ning na GPU a regulariz\u00e1cia. 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