Neural Network and Deep Learning for Beginners: Concept and Implementation Using TensorFlow and Keras

Neural Network and Deep Learning for Beginners: Concept and Implementation Using TensorFlow and Keras
Author :
Publisher : Penerbit USM
Total Pages : 201
Release :
ISBN-10 : 9789674618599
ISBN-13 : 9674618597
Rating : 4/5 (597 Downloads)

Book Synopsis Neural Network and Deep Learning for Beginners: Concept and Implementation Using TensorFlow and Keras by : Putra Sumari

Download or read book Neural Network and Deep Learning for Beginners: Concept and Implementation Using TensorFlow and Keras written by Putra Sumari and published by Penerbit USM. This book was released on with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured guide for beginners to learn about neural networks and yet use them to develop intelligence systems. This book is delivered to readers in three parts. The introduction chapter engages readers in various applications that use neural networks as their backbone. Readers are exposed to the significant use of neural networks in these applications, which represents the intelligence of the human brain. The first part provides readers with important background topics: basic programming and the supervised learning paradigm. This is crucial, as it is the foundation of artificial intelligence application development using neural networks. It gives detailed processes for deep learning system development. The second part explains the mechanism of a neural network in extensive detail. Readers will learn about important components in a neural network, namely the input layer, hidden layer, and output layer. Within that layer, readers are exposed to concepts known as loss function and propagation in detail, which represent a machine learning ability. At the end of this part, readers will also learn the tuning process of a neural network model for best performance. The third part gives examples of case studies. It guides readers on how to develop a real-world intelligence system from scratch. The case studies expose the readers to the processes of assessing and solving the problem, dataset compatibility, model development, training and testing, and finally measuring the accuracy of the system. As readers progress through the whole course, hands-on materials will be provided as part of the practise. The hands-on uses the Python programming language with TensorFlow and Keras libraries.


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