Database design and management involves the process of creating, organizing, and maintaining a database system to efficiently store, retrieve, and manage data. It encompasses various activities, including data modeling, schema design, normalization, indexing, query optimization, transaction management, and data security.At its core, database design focuses on creating a logical and efficient structure for organizing data. This involves identifying the entities, relationships, and attributes that need to be stored in the database and representing them using appropriate data models, such as the...

This book provides a comprehensive exploration of deep learning, starting with the basics of neural networks, including the perceptron algorithm and key techniques like feed-forward and backpropagation, optimization, and regularization. It delves into deep learning foundations, covering important concepts such as gradient descent, backpropagation, and solutions for challenges like the vanishing gradient problem. The book then introduces convolutional neural networks (CNNs), explaining their architectures, convolution and pooling layers, and applications like transfer learning for image...

Dieses Buch bietet eine umfassende Erforschung des Deep Learning, beginnend mit den Grundlagen neuronaler Netze, einschließlich des Perceptron-Algorithmus und Schlüsseltechniken wie Feed-Forward und Backpropagation, Optimierung und Regularisierung. Es befasst sich mit den Grundlagen des Deep Learning und behandelt wichtige Konzepte wie Gradientenabstieg, Backpropagation und Lösungen für Herausforderungen wie das Problem des verschwindenden Gradienten. Anschließend werden Faltungsneuronale Netze (CNNs) vorgestellt und ihre Architekturen, Faltungsschichten und Pooling-Schichten sowie...

Ce livre propose une exploration complète de l'apprentissage profond, en commençant par les bases des réseaux neuronaux, y compris l'algorithme du perceptron et les techniques clés telles que la rétro-propagation, l'optimisation et la régularisation. Il se penche sur les fondements de l'apprentissage profond, couvrant des concepts importants tels que la descente de gradient, la rétropropagation et les solutions à des défis tels que le problème du gradient qui s'évanouit. Le livre présente ensuite les réseaux neuronaux convolutifs (CNN), expliquant leurs architectures, les couches...

This book offers a comprehensive and structured introduction to the foundations, architectures, and applications of deep learning. Beginning with core mathematical concepts such as linear algebra, probability, and optimization, it builds a strong base for understanding modern neural networks. The text explores key ideas like model capacity, bias-variance trade-off, overfitting, and hyperparameter tuning. Readers are then guided through major deep learning architectures, including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) and LSTMs for sequence...