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Deep Leaning Using Keras
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Author(s): Deepa Joshi (Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies (UPES), India), Shahina Anwarul (Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies (UPES), India)and Vidyanand Mishra (Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies (UPES), India)
Copyright: 2020
Pages: 28
Source title:
Machine Learning and Deep Learning in Real-Time Applications
Source Author(s)/Editor(s): Mehul Mahrishi (Swami Keshvanand Institute of Technology, India), Kamal Kant Hiran (Sir Padampat Singhania University, India & Lincoln University College, Malaysia), Gaurav Meena (Central University of Rajasthan, India)and Paawan Sharma (Pandit Deendayal Petroleum University, India)
DOI: 10.4018/978-1-7998-3095-5.ch002
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Abstract
A branch of artificial intelligence (AI) known as deep learning consists of statistical analysis algorithms known as artificial neural networks (ANN) inspired by the structure and function of the brain. The accuracy of predicting a task has tremendously improved with the implementation of deep neural networks, which in turn incorporate deep layers into the model allowing the system to learn complex data. This chapter intends to give a straightforward manual for the complexities of Google's Keras framework that is easy to understand. The basic steps for the installation of Anaconda, CUDA, along with deep learning libraries, specifically Keras and Tensorflow, are discussed. A practical approach towards solving deep learning problems in order to identify objects in CIFAR 10 dataset is explained in detail. This will help the audience in understanding deep learning through substantial practical examples to perceive algorithms instead of theory discussions.
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