Terug naar de homepage
Deze aanbieding is verkocht op di, 22 jul om 1:36 PM.
Math for Deep Learning: What You Need to Know to Understand Neural Networks
Verkocht
Math for Deep Learning: What You Need to Know to Understand Neural Networks
US $21,74US $21,74
di, 22 jul, 13:36di, 22 jul, 13:36
Een soortgelijk object verkopen
Andere objecten van verkoper

Afbeelding 1 van 1

Afbeelding 1 van 1
VERKOCHT
Hebt u iets om te verkopen?
Verkoop het zelf

Math for Deep Learning: What You Need to Know to Understand Neural Networks

Goodwill Industries South Florida
(170060)
Ingeschreven als zakelijke verkoper
Andere objecten van verkoper
Contact opnemen met verkoper
US $21,74
OngeveerEUR 18,79
Objectstaat:
Redelijk
Een soortgelijk object verkopen
Andere objecten van verkoper
    Verzendkosten:
    Gratis Standard Shipping.
    Bevindt zich in: Fort Lauderdale, Florida, Verenigde Staten
    Levering:
    Geschatte levering tussen vr, 8 aug en di, 12 aug tot 94104
    De levertijd wordt geschat met onze eigen methode op basis van onder meer de nabijheid van de koper ten opzichte van de objectlocatie, de geselecteerde verzendservice, en de verzendgeschiedenis van de verkoper. De leveringstermijnen kunnen variëren, vooral gedurende piekperiodes.
    Retourbeleid:
    Geen retourzendingen geaccepteerd.
    Betalingen:
         Diners Club

    Winkel met vertrouwen

    eBay-topverkoper
    Betrouwbare verkoper, snelle verzending en eenvoudige retourzending. Meer weten?- Topverkoper Plus: nieuw venster of tabblad
    Geld-terug-garantie van eBay
    Ontvang het object dat u hebt besteld of krijg uw geld terug. Meer informatieGeld-terug-garantie van eBay - nieuw venster of tabblad
    De verkoper neemt de volledige verantwoordelijkheid voor deze aanbieding.
    eBay-objectnummer:127239657961
    Laatst bijgewerkt op 21 jul 2025 20:20:22 CESTAlle herzieningen bekijkenAlle herzieningen bekijken

    De netto-opbrengst gaat naar Goodwill Industries of South Florida

    Goodwill Industries of South Florida trains and employs people with physical and mental barriers
    • Officiële aanbieding van eBay for Charity Meer weten?
    • Deze verkoop is ten voordele van een goedgekeurde non-profitorganisatie.

    Specificaties

    Objectstaat
    Redelijk: Een boek dat slijtage vertoont. De kaft kan enigszins beschadigd zijn, maar is als geheel ...
    Release Year
    2021
    Book Title
    Math for Deep Learning: What You Need to Know to Understand Ne...
    ISBN
    9781718501904

    Over dit product

    Product Identifiers

    Publisher
    No Starch Press, Incorporated
    ISBN-10
    1718501900
    ISBN-13
    9781718501904
    eBay Product ID (ePID)
    27050380222

    Product Key Features

    Number of Pages
    344 Pages
    Language
    English
    Publication Name
    Math for Deep Learning : What You Need to Know to Understand Neural Networks
    Publication Year
    2021
    Subject
    Neural Networks, General, Calculus
    Type
    Textbook
    Author
    Ronald T. Kneusel
    Subject Area
    Mathematics, Computers, Science
    Format
    Trade Paperback

    Dimensions

    Item Height
    0.9 in
    Item Weight
    23.2 Oz
    Item Length
    9.1 in
    Item Width
    7 in

    Additional Product Features

    Intended Audience
    Trade
    LCCN
    2021-939724
    Reviews
    "What makes Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach." -Ed Scott, Ph.D., Solutions Architect & IT Enthusiast, "An excellent resource for anyone looking to gain a solid foundation in the mathematics underlying deep learning algorithms. The book is accessible, well-organized, and provides clear explanations and practical examples of key mathematical concepts. I highly recommend it to anyone interested in this field." --Daniel Gutierrez, insideBIGDATA "Ronald T. Kneusel has written a handy and compact guide to the mathematics of deep learning. It will be a well-worn reference for equations and algorithms for the student, scientist, and practitioner of neural networks and machine learning. Complete with equations, figures and even sample code in Python, this book is a wonderful mathematical introduction for the reader." --David S. Mazel, Senior Engineer, Regulus-Group "What makes Math for Deep Learning a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach." --Ed Scott, Ph.D., Solutions Architect & IT Enthusiast
    Dewey Edition
    23
    Illustrated
    Yes
    Dewey Decimal
    006.310151
    Table Of Content
    Introduction Chapter 1: Setting the Stage Chapter 2: Probability Chapter 3: More Probability Chapter 4: Statistics Chapter 5: Linear Algebra Chapter 6: More Linear Algebra Chapter 7: Differential Calculus Chapter 8: Matrix Calculus Chapter 9: Data Flow in Neural Networks Chapter 10: Backpropagation Chapter 11: Gradient Descent Appendix: Going Further
    Synopsis
    Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning , you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you'll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta., Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning , you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you'll find coverage of gradient descent including variations commonly used by the deep learning community- SGD, Adam, RMSprop, and Adagrad/Adadelta., To truly understand the power of deep learning, you need to grasp the mathematical concepts that make it tick. Math for Deep Learning will give you a working knowledge of probability, statistics, linear algebra, and differential calculus-the essential math subfields required to practice deep learning successfully. Each subfield is explained with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. The book begins with fundamentals such as Bayes' theorem before progressing to more advanced concepts like training neural networks using vectors, matrices, and derivatives of functions. You'll then put all this math to use as you explore and implement backpropagation and gradient descent- the foundational algorithms that have enabled the Al revolution. You'll learn how to: Use statistics to understand datasets and evaluate models, Apply the rules of probability, Manipulate vectors and matrices to move data through a neural network, Use linear algebra to implement principal component analysis and singular value decomposition, Implement gradient-based optimization techniques like RMSprop, Adagrad, and Adadelta, The core math concepts presented in Math for Deep Learning will give you the foundation you need to unlock the potential of deep learning in your own applications. Book jacket.
    LC Classification Number
    Q325.5

    Objectbeschrijving van de verkoper

    Informatie van zakelijke verkoper

    Ik verklaar dat al mijn verkoopactiviteiten zullen voldoen aan alle wet- en regelgeving van de EU.
    Over deze verkoper

    Goodwill Industries South Florida

    99,2% positieve feedback•619K objecten verkocht

    Lid geworden op jan 2011
    Ingeschreven als zakelijke verkoper
    Welcome to my eBay Store. Please add me to your list of favorite sellers and visit often. Thank you for your business.
    Winkel bezoekenNeem contact met ons op

    Gedetailleerde verkopersbeoordelingen

    Gemiddelde van de afgelopen 12 maanden
    Nauwkeurige beschrijving
    4.9
    Redelijke verzendkosten
    5.0
    Verzendtijd
    5.0
    Communicatie
    5.0

    Feedback verkoper (191.267)

    Alle beoordelingen
    Positief
    Neutraal
    Negatief
      • s***e (1227)- Feedback gegeven door koper.
        Afgelopen maand
        Geverifieerde aankoop
        Fast shipping! Excellent communication! Item as described. Was surprised that the book has no highlighted pages or underlined pages. A+ Reseller! Will buy again!
      Alle feedback weergeven