Книги з теми "Deep neural networks (DNNs)"

Aggarwal, Charu C. Neural Networks and Deep Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94463-0.

Moolayil, Jojo. Learn Keras for Deep Neural Networks. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4240-7.

Caterini, Anthony L., and Dong Eui Chang. Deep Neural Networks in a Mathematical Framework. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1.

Modrzyk, Nicolas. Real-Time IoT Imaging with Deep Neural Networks. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5722-7.

Fingscheidt, Tim, Hanno Gottschalk, and Sebastian Houben, eds. Deep Neural Networks and Data for Automated Driving. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4.

Iba, Hitoshi. Evolutionary Approach to Machine Learning and Deep Neural Networks. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0200-8.

Tetko, Igor V., Věra Kůrková, Pavel Karpov, and Fabian Theis, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3.

Lu, Le, Yefeng Zheng, Gustavo Carneiro, and Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Image Computing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42999-1.

Lu, Le, Xiaosong Wang, Gustavo Carneiro, and Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13969-8.

Graupe, Daniel. Deep Learning Neural Networks. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/10190.

Sze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. Efficient Processing of Deep Neural Networks. Morgan & Claypool Publishers, 2020.

Neural Networks and Deep Learning: A Textbook. Springer, 2018.

Aggarwal, Charu C. Neural Networks and Deep Learning: A Textbook. Springer, 2019.

Luigi Mazzeo, Pier, Srinivasan Ramakrishnan, and Paolo Spagnolo, eds. Visual Object Tracking with Deep Neural Networks. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.80142.

Sugomori, Yusuke, Bostjan Kaluza, Fabio M. Soares, and Alan M. F. Souza. Deep Learning: Practical Neural Networks with Java. Packt Publishing, 2017.

Chang, Dong Eui, and Anthony L. L. Caterini. Deep Neural Networks in a Mathematical Framework. Springer, 2018.

Graupe, Daniel. Deep Learning Neural Networks: Design and Case Studies. World Scientific Publishing Co Pte Ltd, 2016.

Graupe, Daniel. Principles of Artificial Neural Networks: Basic Designs to Deep Learning. World Scientific Publishing Co Pte Ltd, 2019.

Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks. Apress, 2018.

Michelucci, Umberto. Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks. Apress / KP, 2019.

Suresh, Annamalai, R. Udendran, and S. Vimal. Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020.

Deep Learning with TensorFlow: Explore neural networks with Python. Packt Publishing, 2017.

Suresh, Annamalai, R. Udendran, and S. Vimal. Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020.

Suresh, Annamalai, R. Udendran, and S. Vimal. Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020.

Suresh, Annamalai, R. Udendran, and S. Vimal. Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020.

Sejnowski, Terrence J., Tomaso A. Poggio, and Fabio Anselmi. Visual Cortex and Deep Networks: Learning Invariant Representations. MIT Press, 2016.

Michelucci, Umberto. Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection. Apress, 2019.

Introduction to Deep Learning and Neural Networks with Python™. Elsevier, 2021. http://dx.doi.org/10.1016/c2020-0-02367-4.

Theis, Fabian, Věra Kůrková, Igor V. Tetko, and Pavel Karpov. Artificial Neural Networks and Machine Learning – ICANN 2019 : Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, ... Springer, 2019.

Boden, Margaret A. 4. Artificial neural networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0004.

Goyal, Palash. Deep Learning for Natural Language Processing: Creating Neural Networks with Python. Apress, 2018.

Warr, Katy. Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery. O'Reilly Media, Incorporated, 2019.

Kim, Phil. MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. Apress, 2017.

Practical Convolutional Neural Networks: Implement advanced deep learning models using Python. Packt Publishing - ebooks Account, 2018.

Deep Learning: A Practitioner's Approach. O'Reilly Media, 2017.

Iba, Hitoshi. Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks. Springer, 2019.

Iba, Hitoshi. Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks. Springer, 2018.

Moolayil, Jojo. Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python. Apress, 2018.

Saleh, Hyatt. The Deep Learning with PyTorch Workshop: Build Deep Neural Networks and Artificial Intelligence Applications with Pytorch. Packt Publishing, Limited, 2020.

Wang, Xiaosong, Lin Yang, Le Lu, and Gustavo Carneiro. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer, 2019.

(Editor), Takeshi Furuhashi, Shun'Ichi Tano (Editor), and Hans-Arno Jacobsen (Editor), eds. Deep Fusion of Computational and Symbolic Processing. Physica-Verlag Heidelberg, 2001.

Yang, Lin, Le Lu, Yefeng Zheng, and Gustavo Carneiro. Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets. Springer, 2018.

Yang, Lin, Le Lu, Yefeng Zheng, and Gustavo Carneiro. Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets. Springer, 2017.

Deep belief nets in C++ and CUDA C. CreateSpace Independent Publishing Platform, 2015.

Bernico, Mike. Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras. Packt Publishing, 2018.

Hands-On Mathematics for Deep Learning: Build a Solid Mathematical Foundation for Training Efficient Deep Neural Networks. Packt Publishing, Limited, 2020.

Deep learning with keras: Implement neural networks with Keras on Theano and Tensorflow. Birmigham, UK: Packt, 2017.

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms. O'Reilly Media, 2017.

Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing - ebooks Account, 2017.

Modrzyk, Nicolas. Real-Time IoT Imaging with Deep Neural Networks: Using Java on the Raspberry Pi 4. Apress, 2020.