9798196416347 - tensor calculus for deep learning: a practical guide to multidimensional mathematics, optimization, and neural networks von hossain, mir (3 Ergebnisse)
- Softcover
Anbieter: PBShop.store US, Wood Dale, IL, USAPBShop.store US
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 36,46
Versand nach gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes KönigreichPBShop.store UK
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 33,60
EUR 4,87 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 45,00
EUR 62,18 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Taschenbuch. Zustand: Neu. Neuware - 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 mac…hine 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 work>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 JAX>College students in data science, AI, or engineeringMachine learning practitioners who want deeper understandingSelf-taught programmers transitioning into AI>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.
