Handbook of Deep Learning Models, Volume One, Fundamentals

Handbook of Deep Learning Models Volume One: Fundamentals

Master the foundational architectures of modern artificial intelligence. Handbook of Deep Learning Models Volume One: Fundamentals provides a comprehensive and rigorous introduction to the core algorithms and mathematical principles that drive today's most powerful AI systems. Build a rock-solid understanding of neural networks to prepare for advanced machine learning implementations.

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What You Will Learn:

Neural Network Basics: Understand the architecture, mathematics, and mechanics of artificial neurons, perceptrons, and multi-layer networks.

Training Methodologies: Master backpropagation, gradient descent, loss functions, and the optimization algorithms essential for training models effectively.

Core Architectures: Explore the foundational blueprints of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for processing image and sequence data.

Model Evaluation: Learn rigorous techniques for testing, validating, and fine-tuning deep learning models to prevent overfitting and ensure high accuracy.

Who This Book is For: This authoritative handbook is essential for aspiring data scientists, AI researchers, computer science students, and software engineers looking to transition into the machine learning field. It provides the deep theoretical grounding necessary for anyone building a serious career in artificial intelligence.

Product Details:

Format: Digital PDF Download

Authors: Parag Verma; Er. Devarasetty Purna Sankar; Anuj Bhardwaj; Vaibhav Chaudhari; Arnav Pandey; Ankur Dumka

Publisher: CRC Press LLC

Edition: 1st Edition

Publication Date: November 18, 2025

ISBN-13: 9781041102687

ISBN-10: 1041102682