Tensor calculus deep learning di hossain (4 risultati)
- Brossura
Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 33,62
EUR 4,88 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 45,54
EUR 62,18 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware.
- Brossura
- Print on Demand
Da: California Books, Miami, FL, U.S.A.California Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 33,40
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Print on Demand.
- Brossura
- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 38,01
EUR 43,37 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Master the mathematics behind modern AI without getting lost in theory. Most deep learning books either skip the math or bury you in abstract theory. Tensor Calculus for Deep Learning bridges that gap, giving you exactly the mathematical tools you need to understand, build, and debug real m…achine learning models. Whether you're a student, engineer, or self-taught practitioner, this book takes you from core linear algebra and multivariable calculus to the tensor operations that power neural networks step by step, with clarity and purpose. You will learn how gradients flow through networks, how backpropagation really works, and how optimization algorithms shape model performance, all through the lens of tensor calculus. What you will learn: How vectors, matrices, and tensors connect in deep learningThe multivariable chain rule and its role in backpropagationGradient descent, optimization methods, and loss functionsTensor operations including contraction, broadcasting, and einsumThe mathematics behind neural networks, CNNs, RNNs, and transformersHow automatic differentiation engines workAdvanced topics including manifolds and natural gradients Why this book is different: Practical focus: only the math that actually shows up in machine learningStep-by-step solutions with no skipped reasoningWorked examples for every major conceptComplete answers for all exercisesBuilt around real-world frameworks like PyTorch and JAXWho this book is for: College students in data science, AI, or engineeringMachine learning practitioners who want deeper understandingSelf-taught programmers transitioning into AIAnyone who wants to read research papers with confidence If deep learning has ever felt like a black box, this book will give you the mathematical clarity to understand what is really happening inside. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
