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SKU/Artículo: AMZ-B0GKCSDKCW

Deep Learning: From Curiosity To Mastery - Volume 2: An Intuition-First, Hands-On Guide to Building Neural Networks with PyTorch

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Paperback

Kindle

Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.95 kg

Devolución:
Sí

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Nuevo

Producto de:
Amazon

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USA

Sobre este producto

Deep Learning: From Curiosity to MasteryAn Intuition-First, Hands-On Guide to Building Neural Networks with PyTorchDeep learning powers modern artificial intelligence, but learning it often feels overwhelming. Many books move too quickly into dense mathematics or complex code, leaving readers behind. This book was written to change that.Deep Learning: From Curiosity to Mastery is designed to make deep learning accessible, intuitive, and practical for beginners, students, and professionals transitioning into AI and machine learning. It takes an intuition-first, hands-on approach that builds understanding gradually, starting with simple real-world problems and progressing step by step toward modern neural networks used in practice today.Concepts are explained clearly with minimal math upfront and reinforced later through fully working PyTorch implementations. Readers learn not only how neural networks work, but why key design choices matter, developing the confidence to reason about models rather than treating them as black boxes.To support readers from diverse backgrounds, the book includes built-in Python and mathematics primers. Core Python, and deep-learning-specific math as well as Pytorch are introduced gently and only when needed, creating a smooth learning curve without sacrificing rigor. No prior experience in advanced mathematics or machine learning is required to get started.A defining strength of this book is its emphasis on tested, executable code. Accompanying code examples and projects were fully implemented, run, and verified. Readers are encouraged to execute the code, modify it, and experiment freely, moving from passive reading to active model building with confidence.The book is organized into two volumes, carefully structured to support long-term mastery:Volume 1 (Chapters 1–6) focuses on foundations. Readers build intuition, gain Python and Pytorch readiness, and implement neural networks from scratch using PyTorch. Practical projects include house price prediction, weather forecasting, spam detection, and handwriting recognition. This volume also introduces model deployment and essential MLOps concepts for moving models beyond notebooks and into real use.Volume 2 (Chapters 7–9 and Appendix A) advances into modern and emerging deep learning topics: • Chapter 7 explores modern architectures in practice, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers, with clear guidance on when and why to use each. • Chapter 8 explains the math behind learning, connecting intuition, optimization, and training behavior. • Chapter 9 covers advanced areas such as natural language processing, transfer learning, generative adversarial networks (GANs), reinforcement learning, unsupervised learning, explainable AI (XAI) and AI ethics, deep learning security, diffusion models, liquid neural networks, and neural operators. • Appendix A provides deeper mathematical treatment and proofs for readers seeking stronger theoretical grounding.Designed for self-learners, college students, and working professionals alike, this book offers a structured, confidence-building path into deep learning—one that emphasizes understanding, practice, and long-term mastery over intimidation.

AR$96.791

55% OFF

AR$43.997

AR$96.791

55% OFF

AR$43.997

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