Besplatna dostava putem Box Now paketomata i Overseas kurirske službe iznad 69,99 €.

Provjerite status narudžbe

Pridružite se zajednici ljubitelja knjige iz cijelog svijeta i ostvarite mnoštvo pogodnosti. Izradite besplatni račun

Besplatna dostava Overseas kurirskom službom iznad 69.99 €

DPD kurir 3.99 € DPD točka 3.49 € GLS Kurir 4.99 € GLS paketomat 3.99 € Hrvatska pošta 4.99 € Dostava Overseas 4.99 € Box Now 4.49 €

Kontakt

Kako kupovati

Pomoć

Moj račun

▸ Prazno :-(

Besplatna dostava putem Box Now paketomata i Overseas kurirske službe iznad 69,99 €.

Deep Learning for Physics Research

Name: Deep Learning for Physics Research
Brand: WORLD SCIENTIFIC PUB CO INC
SKU: 44214263
Price: 61.76 EUR
Availability: InStock
Author: Jonas Glombitza
ISBN: 9789811285325

Jonas Glombitza

Gregor Kasieczka

Jezik

Engleski

Knjiga Meki uvez

Libristo kod: 44214263

Nakladnici WORLD SCIENTIFIC PUB CO INC, lipanj 2021

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered... Cijeli opis

Libristo kod: 44214263

149 b

61.76 €

Vanjske zalihe Šaljemo za 14-21 dana

Do 30 dana za povrat

Kupci su kupili i

Super-Vilain Ecarlate - Miraculous - achirrir al-ahmar (Arabe) ZAG Knjiga

Meki uvez

9.61 €

Anna Atkins Rolf Sachsse

Tvrdi uvez

14.47 €

Formule 1 color - bloc de coloriage collegium

Meki uvez

11.64 €

Karsai Nei Tsang : masaje depurativo y terapéutico genital Mantak . . . [et al. ] Chia

Meki uvez

16.09 €

Psicologia Social Saul M. Kassin

Meki uvez

50.92 €

Die groten deutschen Passagierschiffe: die Imperator Klasse Dirk Hennings

Adobe ePub DRM

4.75 €

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.

This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning.

Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.