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Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

Hands-on neural networks with TensorFlow 2.0: train, model and deploy the neural networks into your systems using the power of TensorFlow 2.0

Models of Neural Networks IV: Early Vision and Attention

The making of a neuromorphic visual system

Intelligent Networks and Intelligence in Networks: IFIP TC6 WG6.7 International Conference on Intelligent Networks and Intelligence in Networks, 2-5 September 1997, Paris, France

FPGA implementations of neural networks

Biological and Artificial Intelligence Environments

Neuromorphic Circuits for Nanoscale Devices

Real-Time Multi-Chip Neural Network for Cognitive Systems

Neuromorphic Circuits for Nanoscale Devices

Real-Time Multi-Chip Neural Network for Cognitive Systems

Cellular neural networks and visual computing: foundation and applications

Spiking neuron models: single neurons, populations, plasticity

Networks: optimisation and evolution

Clinical applications of artificial neural networks

Machine learning methods in the environmental sciences: neural networks and kernels

Interpretable Machine Learning With Python: Build Explainable, Fair, and Robust High-Performance Models With Hands-on, Real-World Examples

Artificial Vision and Language Processing for Robotics: Create end-to-end systems that can power robots with artificial vision and deep learning techniques

Python reinforcement learning: solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Deep learning with R for beginners: design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions

Unlocking the Secrets of Prompt Engineering: Master the art of creative language generation to accelerate your journey from novice to pro

Generative AI with LangChain: build large language model (LLM) apps with Python, ChatGPT and other LLMs

Machine Learning with SAS Viya

Machine Learning With Sas Viya

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques

Neuro-Symbolic AI: Design transparent and trustworthy systems that understand the world as you do

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