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"Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production"

""Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors"--Page 4 of cover."

"This timely text/​reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: Addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition Discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition Investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples Presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning. Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University."

"Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. --"

"Beras organik adalah beras yang memiliki nilai kesehatan yang tinggi untuk dikonsumsi. Hal ini dikarenakan pembudidayaannya yang dilakukan secara alami dan tidak dipengaruhi oleh bahan-bahan kimia berbahaya yang dapat menyebabkan timbulnya penyakit. Beras organik sudah banyak beredar dipasaran dengan varietas yang beragam serta bersumber dari berbagai wilayah di Indonesia. Penelitian ini dilakukan karena varietas beras organik dari asal daerah yang berbeda memiliki karakteristik yang unik berdasarkan sifat spektral dan spasial untuk dapat dijadikan parameter sistem pengenalan beras organik tanpa merusak sampel dan dapat dilakukan dengan waktu yang relatif cepat. Sistem ini dibuat dengan memanfaatkan pencitraan hyperspectral pada rentang panjang gelombang (400 - 1000 nm) dengan pemodelan klasifikasi pada 5 varietas beras yaitu C4, mentik wangi susu, rojolele, pandan wangi dan situjuh yang berasal dari 3 wilayah di Indonesia yaiu Magelang, Bukittinggi dan Solo. Sistem pengklasifikasian yang dirancang ialah, arsitektur Autoencoder CNN, Alexnet CNN, dan Proposed CNN dengan akurasi rata-rata tertinggi ialah menggunakan Proposed CNN yang memiliki nilai akurasi mencapai 97,59% untuk pengenalan asal daerah beras organik, 93,80% untuk pengenalan varietas beras organik dan 93,89% untuk pengenalan varietas dan beras organik secara bersamaan.

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Organic rice is a type of rice that has a high health value for consumption. This is because of its natural cultivation and also because it's not affected by the hazardous chemicals that can cause disease. Organic rice has been circulating on the market with various varieties and originated from various regions in Indonesia. This study was conducted because organic rice varieties from different regions have unique characteristics based on spectral and spatial properties so that they can be used as recognition system parameters without damaging the sample and can be carried out in a relatively fast time. This system was created by utilizing hyperspectral imaging in the wavelength range (400 - 1000 nm) with classification modeling in 5 rice varieties is C4, Mentik Wangi Susu, Rojolele, Pandan Wangi and Situjuh from 3 regions in Indonesia that is Magelang, Bukittinggi and Solo. The classification using model of CNN Architectur Autoencoder, Alexnet CNN, and Proposed CNN. The classification results with the highest average accuracy is the classification that uses the Proposed CNN, which has an accuracy value of 97.59% for the introduction of the regional origins of the organic rice, 93.80% for the introduction of the varieties of the organic rice, and 93.89% for the introduction of the varieties and the regional origins simultaneously.

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"This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results."

"With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book"