| Call Number (LC) | Title | Results |
|---|---|---|
| QA76.81 .G37 1998 | Neural networks : an introductory guide for social scientists / | 1 |
| QA76.85 .U54 1987 | The fifth generation fallacy : why Japan is betting its future on artificial intelligence / | 1 |
| QA76.87 |
Zeroing Neural Networks : Finite-Time Convergence Design, Analysis and Applications / Neural Search, from Prototype to Production with Jina : Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease / Use tweaks and optimization techniques for data generation / Real-Time IoT Imaging with Deep Neural Networks : Using Java on the Raspberry Pi 4 / Deep belief nets in C++ and CUDA C. Introduction to deep learning using PyTorch : create simple neural networks in Python using PyTorch / Use autoencoders to denoise data / Random search and reproducibility for neural architecture search / Artificial neural networks with TensorFlow 2 : ANN architecture machine learning projects / Hands-on question answering systems with BERT : applications in neural networks and natural language processing / Hands-on neural network programming with C# : add powerful neural network capabilities to your C# enterprise applications / The deep learning with PyTorch workshop. State of the art in neural networks and their applications. Zeng qiang shen du shen jing wang luo = Strengthening deep neural networks / PyTorch kompakt : Syntax, Design Patterns und Codebeispiele für Deep-Learning-Modelle / Artificial neural networks with Java : tools for building neural network applications / Neural networks for electronics hobbyists : a non-technical project-based introduction / PyTorch recipes : a problem-solution approach / Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras / Deep learning with PyTorch quick start guide : learn to train and deploy neural network models in Python / Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes / Deep belief nets in C++ and CUDA C : Deep learning with PyTorch / Applied deep learning : a case-based approach to understanding neural networks / Semi-Empirical Neural Network Modeling and Digital Twins Development / Neural network modeling and identification of dynamical systems / Neural Networks in Unity : C# Programming for Windows 10 / Neuronale Netze selbst programmieren : ein verständlicher Einstieg mit Python / Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python / Artificial neural network for software reliability prediction / Understanding convolutional neural networks (CNNs) : learn how to implement CNNs to generate visualizations / Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules. Neural networks. Practical convolutional neural networks : implement advanced deep learning models using Python / Handbook of neural computation / Una aproximacion practica a las redes neuronales artificiales Introduction to deep learning : concepts and fundamentals / Neuro-inspired Information Processing. Recommendation systems. Avoiding the pitfalls of deep learning : solving model overfitting with regularization and dropout / Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines / Coding neural networks with TensorFlow 2.0. Applying the idiomatic design pattern to convolutional neural networks / Convolutional neural networks with Swift for TensorFlow : image recognition and dataset categorization / Neural networks and deep learning : a textbook / Artificial neural networks and machine learning -- ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings. Advances in artificial neural systems. Keras shen du xue xi shi zhan = Deep learning with Keras / Shen jing wang luo suan fa yu shi xian : ji yu Java yu yan = Neural network programming with Java / Neurocomputing. Network : computation in neural systems / Neurocomputing : learning, architectures, and modeling / Information theoretic neural computation / Hands-on Java deep learning for computer vision : implement machine learning and neural network methodologies to perform computer vision-related tasks / Multilayer perceptrons : theory and applications / Modern Computer Vision with Pytorch : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. Hands-on neuroevolution with Python : build high-performing artificial neural network architectures using neuroevolution-based algorithms / Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow / The machine learning workshop. New developments in artificial neural networks research / Artificial neural network training and software implementation techniques / Neural computation and particle accelerators : research, technology and applications / Cellular neural networks and their applications : proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and Their Applications : Institute of Applied Physics, Johann Wolfgang Goethe-University, Frankfurt, Germany, 22-24 July, 2002 / Artificial neural networks / Recent advances in artificial intelligence research / Fuzzy neural networks for real time control applications : concepts, modeling and algorithms for fast learning / Neural network driven artificial intelligence : decision making based on fuzzy logic / Diffuse algorithms for neural and neuro-fuzzy networks : with applications in control engineering and signal processing / Neural networks with Keras cookbook : over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots. Applied Neural Networks and Soft Computing. Blind equalization in neural networks : theory, algorithms and application / Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Recurrent neural networks with Python Quick Start Guide : sequential learning and language modeling with TensorFlow / Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / PyTorch shen du xue xi = Deep learning with PyTorch / Intelligence emerging : adaptivity and search in evolving neural systems / Deep neural networks and applications The deep learning with Keras workshop : learn how to define and train neural network models with just a few lines of code / Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / Real-Time Multi-Chip Neural Network for Cognitive Systems Mastering PyTorch : create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond / How smart machines think / Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023). |
86 |
| QA76.87.A325 | Neural networks / | 1 |
| QA76.87 .A325 1999eb | Neural networks / | 1 |
| QA76.87 .A3726 1993eb | Advances in pattern recognition systems using neural network technologies / | 1 |
| QA76.87 .A38 2010eb | Advances in cognitive systems / | 1 |
| QA76.87 .A58 1999 | Neural network learning : theoretical foundations / | 1 |
| QA76.87 .A62 2001eb | Application of neural networks and other learning technologies in process engineering / | 1 |
| QA76.87 .A65 1993 | Application of neural networks to modelling and control / | 1 |
| QA76.87 .A67 1992 | Applications of artificial neural networks III : 21-24 April 1992, Orlando, Florida / | 1 |
| QA76.87 .A74 1992 | Artificial neural networks : concepts and theory / | 1 |
| QA76.87 .A7432 1994 | Artificial neural networks for intelligent manufacturing / | 1 |
| QA76.87 .A77 1988 | Artificial neural networks : theoretical concepts / | 1 |
| QA76.87 .A785 2017 | Artificial neural networks : new research / | 1 |
| QA76.87 .A88 1993 | Associative neural memories : theory and implementation / | 1 |
| QA76.87 .B47 1996 | Neuro-dynamic programming / | 1 |
| QA76.87 .B55 1996 | Data mining with neural networks : solving business problems--from application development to decision support / | 1 |
| QA76.87 .B57 2017 | Artificial neural network for software reliability prediction / | 1 |
| QA76.87 .B576 2009eb | Neural networks in atmospheric remote sensing / | 2 |
