Products related to Neural:
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Neural Networks
A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practicesNeural Networks proposes to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same name.If so-called machine learning comprises a statistical approach to pattern extraction, then neural networks can be defined as a biologically inspired model that relies on probabilistically weighted neuron-like units to identify such patterns.Far from signaling the ultimate convergence of human and machine intelligence, however, neural networks highlight the technologization of neurophysiology that characterizes virtually all strands of neuroscientific and AI research of the past century.Taking this traffic as its starting point, this volume explores how cognition came to be constructed as essentially computational in nature, to the point of underwriting a technologized view of human biology, psychology, and sociability, and how countermovements provide resources for thinking otherwise.
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Neural Machine Translation
Deep learning is revolutionizing how machine translation systems are built today.This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications.Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems.The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization.Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
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Neural Networks for Babies
Fans of Chris Ferrie's ABCs of Economics, ABCs of Space, and Organic Chemistry for Babies will love this introduction to neural networks for babies and toddlers!Help your future genius become the smartest baby in the room!It only takes a small spark to ignite a child's mind. Neural Networks for Babies by Chris Ferrie is a colorfully simple introduction to the study of how machines and computing systems are created in a way that was inspired by the biological neural networks in animal and human brains.With scientific and mathematical information from an expert, this installment of the Baby University board book series is the perfect book for enlightening the next generation of geniuses.After all, it's never too early to become a scientist!If you're looking for programming for babies, coding for babies, or more Baby University board books to surprise your little one, look no further!Neural Networks for Babies offers fun early learning for your little scientist!
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Criticality in Neural Systems
Leading authorities in the field review current experimental and theoretical knowledge oncriticality and brain function. The book begins by summarizing experimental evidence for criticality and self-organizedcriticality in the brain.Subsequently, important breakthroughs in modeling of critical neuronal circuits and how to establish self-organized criticality in the brain are described. A milestone publication, defining upcoming directions of research in this new fi eld and set to become the primary source of information on the brain and criticality.
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What is neural computation?
Neural computation refers to the process by which the brain and nervous system process and transmit information. It involves the complex interactions between neurons, which are the basic building blocks of the nervous system. Neural computation encompasses a wide range of functions, including sensory perception, motor control, learning, and memory. This field of study seeks to understand how neural networks process information and how these processes can be replicated or simulated in artificial systems.
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What are neural mechanisms?
Neural mechanisms refer to the processes and pathways in the brain that underlie various cognitive functions and behaviors. These mechanisms involve the communication between neurons through electrical and chemical signals, as well as the formation and strengthening of neural connections. Understanding neural mechanisms is crucial for studying how the brain processes information, controls behavior, and responds to different stimuli. Researchers use various techniques, such as neuroimaging and electrophysiology, to investigate these neural mechanisms and gain insights into brain function.
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How does neural computation work?
Neural computation refers to the process by which the brain processes and analyzes information. It involves the transmission of electrical signals between neurons, which are specialized cells that make up the nervous system. These signals are passed through synapses, or connections between neurons, and can be either excitatory or inhibitory. The brain integrates and processes these signals to generate responses and behaviors. Overall, neural computation is a complex and dynamic process that underlies all cognitive functions and behaviors.
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How to program neural networks?
To program neural networks, you can use programming languages like Python and libraries such as TensorFlow or PyTorch. First, you need to define the architecture of the neural network by specifying the number of layers, types of activation functions, and the number of neurons in each layer. Then, you can compile the model by choosing an optimizer and a loss function. Finally, you can train the neural network using a dataset by fitting the model to the data and adjusting the weights through backpropagation.
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Neural DSP Nano Cortex
The Neural DSP Nano Cortex is an ultra-compact powerhouse that brings Neural Capture technology and a comprehensive signal chain into the palm of your hand. With the ability to create Neural Captures on-device in under five minutes it offers unparalleled convenience for guitarists and bassists. Featuring seven fully customisable device blocks including Adaptive Gate Chorus and Analog Delay along with a built-in IR loader the Nano Cortex delivers studio-quality sound in a portable format. Despite its small size this versatile unit functions as a full-fledged audio interface with USB-C connectivity allowing for seamless recording at 24-bit 48kHz resolution. With Bluetooth for wireless preset management and firmware updates plus Cortex Cloud integration to access thousands of captures and presets the Nano Cortex provides endless creative potential in a compact user-friendly design.
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Neural DSP Quad Cortex
The Neural DSP Quad Cortex is a state-of-the-art amp modeller and multi-effects unit. Armed with an ultra-powerful Quad-Core 1.8GHz DSP system the Quad Cortex blends the latest in technology innovation with music creation. The compact featherlight design makes it easy to transport yet it is resilient enough to withstand the rigors of touring. The Quad Cortex is not just about power and portability though. It excels in the realm of accuracy utilising machine learning to capture the essence of your favourite gear with unparalleled precision. The vast library of effects amps cabs and microphones provides endless options for tone customisation truly making this an all-in-one tool for musicians.
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Principles of Neural Design
Two distinguished neuroscientists distil general principles from more than a century of scientific study, "reverse engineering" the brain to understand its design. Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods.A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing.In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently.Setting out to "reverse engineer" the brain-disassembling it to understand it-Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm.They examine bigger brains and the advantages of "anticipatory regulation"; identify constraints on neural design and the need to "nanofy"; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction.They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes "save only what is needed."Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known.Their distinctive contribution is to gather a coherent set of basic rules and exemplify them across spatial and functional scales.
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Evolving Neural Crest Cells
Vertebrates possess lineage-specific characteristics.These include paired anterior sense organs and a robust, modular head skeleton built of cellular cartilage and bone.All of these structures are derived, at least partly, from an embryonic tissue unique vertebrates - the neural crest.The evolutionary history of the neural crest, and neural crest cells, has been difficult to reconstruct.This volume will use a comparative approach to survey the development of the neural crest in vertebrates, and neural crest-like cells, across the metazoa.This information will be used to reveal neural crest evolution and identify the genomic, genetic, and gene-regulatory changes that drove them. Key selling features:Summarizes the data regarding neural crest cells and nerural crest derivativesUses a broad-based comparative approachSuggests hypothesis that the origin of neural crest cells involved the novel co-activation of ancient metazoan gene programs in neural border cellsIllustrates how the emergences of neural crest made possible the diversification of vertebrate heads
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What is neural computation 2?
Neural computation 2 refers to the process of information processing and decision-making within the brain using neural networks. It involves the complex interactions between neurons, synapses, and neurotransmitters to perform tasks such as learning, memory, and problem-solving. Neural computation 2 also encompasses the study of artificial neural networks, which are computational models inspired by the structure and function of the brain, and are used in various applications such as pattern recognition, machine learning, and artificial intelligence.
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Can artificial neural networks have feelings?
No, artificial neural networks do not have feelings. They are computational models designed to process and analyze data, but they do not possess consciousness or emotions like humans do. Neural networks operate based on mathematical algorithms and patterns, without the ability to experience emotions or feelings.
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How does neural information processing work?
Neural information processing involves the transmission of signals between neurons in the brain. When a neuron receives a signal, it processes the information and then transmits a new signal to other neurons. This process involves the release of neurotransmitters at the synapses, which are the connections between neurons. The strength and timing of these signals play a crucial role in determining how information is processed and ultimately leads to various cognitive and behavioral outcomes. Overall, neural information processing is a complex and dynamic system that underlies all brain functions.
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How do you program neural networks?
To program neural networks, you can use programming languages such as Python and libraries like TensorFlow, Keras, or PyTorch. First, you define the architecture of the neural network by specifying the number of layers, the number of neurons in each layer, and the activation functions. Then, you compile the model by specifying the loss function, optimizer, and metrics. Finally, you train the neural network by providing input data and corresponding output labels, and then evaluate its performance on a separate test dataset. This process involves adjusting the model's parameters through backpropagation to minimize the loss and improve its predictive accuracy.
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