Books on the topic 'Deep learning neural network'

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

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.

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.

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.

Thrun, Sebastian. Explanation-Based Neural Network Learning. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1381-6.

Gallant, Stephen I. Neural network learning and expert systems. Cambridge, Mass: MIT Press, 1993.

Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1444-5.

Thrun, Sebastian. Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Boston, MA: Springer US, 1996.

Thrun, Sebastian. Explanation-based neural network learning: A lifelong learning approach. Boston: Kluwer Academic Publishers, 1996.

Schmajuk, Nestor A. Animal learning and cognition: A neural network approach. Cambridge [England]: Cambridge University Press, 1997.

Neural network design and the complexity of learning. Cambridge, Mass: MIT Press, 1990.

Modular learning in neural networks: A modularized approach to neural network classification. New York, NY: Wiley, 1992.

Radi, Amr Mohamed. Discovery of neural network learning rules using genetic programming. Birmingham: University of Birmingham, 2000.

Choo, Kiam. Learning hyperparameters for neural network models using Hamiltonian dynamics. Ottawa: National Library of Canada, 2000.

Kuan, Chung-Ming. Recursive M-estimation, nonlinear regression and neural network learning with dependent observations. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1990.

Zalzala, A. M. S. A Neural network methodology for engineering applications: The learning control of robotic systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

Gluck, Mark A. Gateway to memory: An introduction to neural network modeling of the hippocampus and learning. Cambridge, Mass: MIT Press, 2001.

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

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

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

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

Di, Wei, Anurag Bhardwaj, and Jianing Wei. Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling. Packt Publishing, 2018.

Kumar, Rahul, Matthew Lamons, and Abhishek Nagaraja. Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems. Packt Publishing, 2018.

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

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow. 2nd ed. Packt Publishing, 2019.

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

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

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.

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

Verhelst, Marian, Bert Moons, and Daniel Bankman. Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing. Springer, 2019.

Verhelst, Marian, Bert Moons, and Daniel Bankman. Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing. Springer, 2018.

Silaparasetty, Vinita. Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras. Apress, 2020.

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

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.

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.

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch. Packt Publishing, 2018.

Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python. Packt Publishing, 2017.

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

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

Bali, Raghav, Dipanjan Sarkar, and Tamoghna Ghosh. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing, 2018.

R Deep Learning Projects: Master the techniques to design and develop neural network models in R. Packt Publishing, 2018.

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

Raj, Rahul. Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j. Packt Publishing, 2019.

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.

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