Articles liés à Liquid Neural Networks: 33 Comprehensively Commented...
Liquid Neural Networks: 33 Comprehensively Commented Python Implementations of Liquid Neural Networks(LNNs) - Couverture souple
Synopsis
Experience the Next Evolution in Neural Networks
Gain the competitive edge in data science and AI with an authoritative guide to a revolutionary approach: Liquid Neural Networks. Derived from breakthroughs in continuous-time modeling, these dynamic architectures leverage differential equations to adapt their hidden states in real time. Ideal for scientists, engineers, and academics alike, this resource unites provides 33 Python implementations in one comprehensive volume.
Why Liquid Neural Networks?
Liquid Neural Networks excel at:
- Continuous Adaptation: Hidden states evolve based on ever-changing inputs, ensuring robust performance in non-stationary and irregular time-series data.
- Unparalleled Temporal Modeling: Differential equations under the hood capture subtle transitions and long-term contexts better than traditional discrete architectures.
- Real-World Impact: From anomaly detection in streams to real-time control systems, this approach seamlessly improves accuracy and responsiveness.
What You’ll Discover
Inside this academically rigorous yet practitioner-friendly collection of code implementations, you will explore how to:
- Construct Next-Level Time-Series Forecasting
Harness adaptive ODE-driven memory states that thrive on volatile datasets such as finance or energy consumption. - Detect Anomalies in High-Velocity Data Streams
Leverage continuous updates to maintain heightened sensitivity to sudden, unpredictable shifts. - Master Sensor Data Processing for IoT
Integrate multi-modal sensor signals into a single adaptive architecture for robust, real-time insights. - Reinvent Reinforcement Learning Policies
Simplify partially observable problems by letting the network’s hidden states naturally refine over time. - Refine Complex Robotics Control
Achieve smoother, more reactive motion planning by coupling sensor dynamics with liquid recurrent units. - Implement Advanced Healthcare and Predictive Maintenance
Reduce downtime and improve patient outcomes by identifying critical changes in continuous physiological or mechanical signals.
And much more— with comprehensive Python code to facilitate hands-on experimentation and accelerate your breakthroughs.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 31,49
EUR 4,61 expédition depuis Royaume-Uni vers France
Destinations, frais et délaisRésultats de recherche pour Liquid Neural Networks: 33 Comprehensively Commented...
Image d'archives
Liquid Neural Networks: 33 Comprehensively Commented Python Implementations of Liquid Neural Networks(LNNs) (Stochastic Sorcerers)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9798307622704_new
Quantité disponible : Plus de 20 disponibles
Image d'archives
Liquid Neural Networks: 33 Comprehensively Commented Python Implementations of Liquid Neural Networks(LNNs) (Stochastic Sorcerers)
impression à la demandeVendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur I-9798307622704
Quantité disponible : Plus de 20 disponibles
Image d'archives
Flux, J: Liquid Neural Networks
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware - Why Liquid Neural Networks. N° de réf. du vendeur 9798307622704
Quantité disponible : 2 disponible(s)
Image d'archives
Liquid Neural Networks (Paperback)
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Experience the Next Evolution in Neural Networks Gain the competitive edge in data science and AI with an authoritative guide to a revolutionary approach: Liquid Neural Networks. Derived from breakthroughs in continuous-time modeling, these dynamic architectures leverage differential equations to adapt their hidden states in real time. Ideal for scientists, engineers, and academics alike, this resource unites provides 33 Python implementations in one comprehensive volume. Why Liquid Neural Networks?Liquid Neural Networks excel at: Continuous Adaptation: Hidden states evolve based on ever-changing inputs, ensuring robust performance in non-stationary and irregular time-series data.Unparalleled Temporal Modeling: Differential equations under the hood capture subtle transitions and long-term contexts better than traditional discrete architectures.Real-World Impact: From anomaly detection in streams to real-time control systems, this approach seamlessly improves accuracy and responsiveness. What You'll DiscoverInside this academically rigorous yet practitioner-friendly collection of code implementations, you will explore how to: Construct Next-Level Time-Series ForecastingHarness adaptive ODE-driven memory states that thrive on volatile datasets such as finance or energy consumption.Detect Anomalies in High-Velocity Data StreamsLeverage continuous updates to maintain heightened sensitivity to sudden, unpredictable shifts.Master Sensor Data Processing for IoTIntegrate multi-modal sensor signals into a single adaptive architecture for robust, real-time insights.Reinvent Reinforcement Learning PoliciesSimplify partially observable problems by letting the network's hidden states naturally refine over time.Refine Complex Robotics ControlAchieve smoother, more reactive motion planning by coupling sensor dynamics with liquid recurrent units.Implement Advanced Healthcare and Predictive MaintenanceReduce downtime and improve patient outcomes by identifying critical changes in continuous physiological or mechanical signals. And much more- with comprehensive Python code to facilitate hands-on experimentation and accelerate your breakthroughs. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798307622704
Quantité disponible : 1 disponible(s)
Image d'archives
Liquid Neural Networks (Paperback)
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Experience the Next Evolution in Neural Networks Gain the competitive edge in data science and AI with an authoritative guide to a revolutionary approach: Liquid Neural Networks. Derived from breakthroughs in continuous-time modeling, these dynamic architectures leverage differential equations to adapt their hidden states in real time. Ideal for scientists, engineers, and academics alike, this resource unites provides 33 Python implementations in one comprehensive volume. Why Liquid Neural Networks?Liquid Neural Networks excel at: Continuous Adaptation: Hidden states evolve based on ever-changing inputs, ensuring robust performance in non-stationary and irregular time-series data.Unparalleled Temporal Modeling: Differential equations under the hood capture subtle transitions and long-term contexts better than traditional discrete architectures.Real-World Impact: From anomaly detection in streams to real-time control systems, this approach seamlessly improves accuracy and responsiveness. What You'll DiscoverInside this academically rigorous yet practitioner-friendly collection of code implementations, you will explore how to: Construct Next-Level Time-Series ForecastingHarness adaptive ODE-driven memory states that thrive on volatile datasets such as finance or energy consumption.Detect Anomalies in High-Velocity Data StreamsLeverage continuous updates to maintain heightened sensitivity to sudden, unpredictable shifts.Master Sensor Data Processing for IoTIntegrate multi-modal sensor signals into a single adaptive architecture for robust, real-time insights.Reinvent Reinforcement Learning PoliciesSimplify partially observable problems by letting the network's hidden states naturally refine over time.Refine Complex Robotics ControlAchieve smoother, more reactive motion planning by coupling sensor dynamics with liquid recurrent units.Implement Advanced Healthcare and Predictive MaintenanceReduce downtime and improve patient outcomes by identifying critical changes in continuous physiological or mechanical signals. And much more- with comprehensive Python code to facilitate hands-on experimentation and accelerate your breakthroughs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798307622704
Quantité disponible : 1 disponible(s)