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A Comprehensive Guide to Theory and Implementation.Bridging the gap between theory and practice with this extensive guide to neural networks, featuring parallel implementations in both Python and MATLAB.Navigating the complex landscape of neural networks requires not only a firm grasp of theoretical foundations but also the practical skills to implement them effectively. Practical Neural Networks in Python and MATLAB is designed to be a definitive resource, offering a unique dual-language approach to mastering these powerful models.Key Features:A Dual-Language, Integrated Approach: This book provides a side-by-side exploration of neural networks in both Python and MATLAB. This methodology allows you to leverage Python's rich deep learning ecosystem (TensorFlow, Keras, PyTorch) and MATLAB's specialized toolboxes, giving you the flexibility to work within your preferred environment or across different project requirements.Comprehensive Coverage of Algorithms and Architectures: Move beyond basic backpropagation. The text provides a systematic review of fundamental and advanced training algorithms, including Gradient Descent, Newton's Method, Levenberg-Marquardt, Recursive Least Squares (RLS), and metaheuristics like Genetic Algorithms and Particle Swarm Optimization. Furthermore, it offers a detailed survey of over 25 major neural network architectures, from foundational Perceptrons and Feedforward Networks to advanced systems like CNNs, RNNs (LSTM, GRU), Autoencoders, GANs, and Deep Belief Networks.Practical, Code-Oriented Learning: Each concept and architecture is accompanied by ready-to-run code examples. This practical focus ensures that you can immediately translate theoretical understanding into functional code, experiment with parameters, and adapt the implementations to your own unique challenges.Real-World Application and Case Studies: The learning is grounded in practicality through diverse case studies across multiple domains. You will find applications in medical diagnostics (e.g., diabetes classification), time-series forecasting (e.g., air quality prediction), system identification, natural language processing, and more. These examples provide complete pipelines from data preprocessing and model training to evaluation and visualization.This Book is Ideal For:University students and researchers in Computer Science, Artificial Intelligence, Engineering, and related fields.R&D engineers and scientists working in algorithm development, data analysis, and intelligent systems.Any practitioner seeking a thorough, hands-on understanding of neural networks with the flexibility to work in both Python and MATLAB environments.In essence, Practical Neural Networks in Python and MATLAB serves as an invaluable companion for anyone looking to deepen their expertise in neural networks. It is more than a textbook; it is a practical toolkit designed to accelerate your research, enhance your projects, and provide a clear, comprehensive reference for the key architectures and algorithms shaping the field of AI today.

A Comprehensive Guide to Theory and Implementation.Bridging the gap between theory and practice with this extensive guide to neural networks, featuring parallel implementations in both Python and MATLAB.Navigating the complex landscape of neural networks requires not only a firm grasp of theoretical foundations but also the practical skills to implement them effectively. Practical Neural Networks in Python and MATLAB is designed to be a definitive resource, offering a unique dual-language approach to mastering these powerful models.Key Features:A Dual-Language, Integrated Approach: This book provides a side-by-side exploration of neural networks in both Python and MATLAB. This methodology allows you to leverage Python's rich deep learning ecosystem (TensorFlow, Keras, PyTorch) and MATLAB's specialized toolboxes, giving you the flexibility to work within your preferred environment or across different project requirements.Comprehensive Coverage of Algorithms and Architectures: Move beyond basic backpropagation. The text provides a systematic review of fundamental and advanced training algorithms, including Gradient Descent, Newton's Method, Levenberg-Marquardt, Recursive Least Squares (RLS), and metaheuristics like Genetic Algorithms and Particle Swarm Optimization. Furthermore, it offers a detailed survey of over 25 major neural network architectures, from foundational Perceptrons and Feedforward Networks to advanced systems like CNNs, RNNs (LSTM, GRU), Autoencoders, GANs, and Deep Belief Networks.Practical, Code-Oriented Learning: Each concept and architecture is accompanied by ready-to-run code examples. This practical focus ensures that you can immediately translate theoretical understanding into functional code, experiment with parameters, and adapt the implementations to your own unique challenges.Real-World Application and Case Studies: The learning is grounded in practicality through diverse case studies across multiple domains. You will find applications in medical diagnostics (e.g., diabetes classification), time-series forecasting (e.g., air quality prediction), system identification, natural language processing, and more. These examples provide complete pipelines from data preprocessing and model training to evaluation and visualization.This Book is Ideal For:University students and researchers in Computer Science, Artificial Intelligence, Engineering, and related fields.R&D engineers and scientists working in algorithm development, data analysis, and intelligent systems.Any practitioner seeking a thorough, hands-on understanding of neural networks with the flexibility to work in both Python and MATLAB environments.In essence, Practical Neural Networks in Python and MATLAB serves as an invaluable companion for anyone looking to deepen their expertise in neural networks. It is more than a textbook; it is a practical toolkit designed to accelerate your research, enhance your projects, and provide a clear, comprehensive reference for the key architectures and algorithms shaping the field of AI today.

Calculations and Simulations of Low-Dimensional Materials A comprehensive guide to methods for calculating and simulating the properties of low-dimensional materials Two-dimensional materials are those, such as graphene and 2D oxides, whose thickness is so small as to approach the atomic scale. Potential applications for these materials exist in an enormous range of scientific and industrial fields. A previous era of low-dimensional materials focused on direct experimentation to demonstrate the properties, reactions, and potential applications of these materials; however, in recent years, calculation and simulation have been shown to have considerable predictive power, reducing the period between design and deployment of these potentially critical materials. Calculations and Simulations of Low-Dimensional Materials offers the first comprehensive survey of this exciting new approach to low-dimensional materials. It guides readers through the foundational physics and through a range of calculation and simulation methods, each with different predictive capacities. Mastery of these methods will enable readers to narrowly tailor the properties of particular materials towards real-world applications, providing confidence in the underlying mechanics and in the range of possible outcomes. Calculations and Simulations of Low-Dimensional Materials readers will also find: Broad coverage of material properties, including electronic, spin, magnetic, photonic, optical, electrochemical and transport propertiesDiscussion of potential applications in areas such as electronics, spintronics, and valleytronics Examination of further potential applications regarding quantum Hall phase, photonics, optoelectronics, multiferroic, and photocatalysisCalculations and Simulations of Low-Dimensional Materials is a useful reference for materials scientists, electrochemists, inorganic chemists, physical chemists, photochemists, and the libraries that support these professions.

Calculations and Simulations of Low-Dimensional Materials

A comprehensive guide to methods for calculating and simulating the properties of low-dimensional materials

Two-dimensional materials are those, such as graphene and 2D oxides, whose thickness is so small as to approach the atomic scale. Potential applications for these materials exist in an enormous range of scientific and industrial fields. A previous era of low-dimensional materials focused on direct experimentation to demonstrate the properties, reactions, and potential applications of these materials; however, in recent years, calculation and simulation have been shown to have considerable predictive power, reducing the period between design and deployment of these potentially critical materials.

Calculations and Simulations of Low-Dimensional Materials offers the first comprehensive survey of this exciting new approach to low-dimensional materials. It guides readers through the foundational physics and through a range of calculation and simulation methods, each with different predictive capacities. Mastery of these methods will enable readers to narrowly tailor the properties of particular materials towards real-world applications, providing confidence in the underlying mechanics and in the range of possible outcomes.

Calculations and Simulations of Low-Dimensional Materials readers will also find:

Broad coverage of material properties, including electronic, spin, magnetic, photonic, optical, electrochemical and transport properties Discussion of potential applications in areas such as electronics, spintronics, and valleytronics Examination of further potential applications regarding quantum Hall phase, photonics, optoelectronics, multiferroic, and photocatalysis

Calculations and Simulations of Low-Dimensional Materials is a useful reference for materials scientists, electrochemists, inorganic chemists, physical chemists, photochemists, and the libraries that support these professions.