Products related to Neural:
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Neural Encoder
UBX127's latest offering, Neural Encoder, is a kaleidoscopic journey through the electronic music landscape that leaves listeners dizzy with delight. The Berlin-based producer has crafted an album that is as unpredictable as it is infectious, blending elements of techno, acid house, and ambient soundscapes with a mischievous glee that is impossible to resist. From the opening track "Ethereal Ripples" to the closing notes of "Neural Resonance," UBX127 takes us on a whirlwind tour of his sonic playground, layering pulsating beats and squelchy synths with a deft touch that is both playful and precise. Tracks like "Binary Dreams" and "Circuit Bender" showcase his knack for crafting hypnotic grooves that beg to be danced to, while more introspective moments like "Static Echo" and "Ghost in the Machine" reveal a depth and complexity that is truly impressive. Neural Encoder is a sonic rollercoaster that never lets up, each track building on the last to create a rich and engaging listening experience. UBX127's production skills are on full display here, as he deftly navigates the narrow line between chaos and control with a finesse that is truly awe-inspiring. In short, Neural Encoder is a triumph of modern electronic music, a bold and brash statement from a producer who is unafraid to push boundaries and defy expectations. UBX127 has crafted an album that is as exhilarating as it is innovative, a must-listen for fans of cutting-edge electronic music everywhere.
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Hands-On Neural Network Programming with C# : Add powerful neural network capabilities to your C# enterprise applications
Create and unleash the power of neural networks by implementing C# and .Net codeKey FeaturesGet a strong foundation of neural networks with access to various machine learning and deep learning librariesReal-world case studies illustrating various neural network techniques and architectures used by practitionersCutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many moreBook DescriptionNeural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence.The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp.This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks.You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord.You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders.You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications. What you will learnUnderstand perceptrons and how to implement them in C#Learn how to train and visualize a neural network using cognitive servicesPerform image recognition for detecting and labeling objects using C# and TensorFlowSharpDetect specific image characteristics such as a face using Accord.NetDemonstrate particle swarm optimization using a simple XOR problem and EncogTrain convolutional neural networks using ConvNetSharpFind optimal parameters for your neural network functions using numeric and heuristic optimization techniques. Who this book is forThis book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#.Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book
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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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Which programming language is best suited for programming neural networks?
Python is widely considered the best programming language for programming neural networks. It has a rich ecosystem of libraries and frameworks specifically designed for machine learning and neural network development, such as TensorFlow, Keras, and PyTorch. Python's simplicity, readability, and flexibility make it an ideal choice for implementing complex neural network algorithms and models. Additionally, Python has a large and active community, providing ample resources and support for developers working on neural network projects.
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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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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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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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Neural Control of Speech
A comprehensive and unified account of the neural computations underlying speech production, offering a theoretical framework bridging the behavioral and the neurological literatures. In this book, Frank Guenther offers a comprehensive, unified account of the neural computations underlying speech production, with an emphasis on speech motor control rather than linguistic content.Guenther focuses on the brain mechanisms responsible for commanding the musculature of the vocal tract to produce articulations that result in an acoustic signal conveying a desired string of syllables.Guenther provides neuroanatomical and neurophysiological descriptions of the primary brain structures involved in speech production, looking particularly at the cerebral cortex and its interactions with the cerebellum and basal ganglia, using basic concepts of control theory (accompanied by nontechnical explanations) to explore the computations performed by these brain regions. Guenther offers a detailed theoretical framework to account for a broad range of both behavioral and neurological data on the production of speech.He discusses such topics as the goals of the neural controller of speech; neural mechanisms involved in producing both short and long utterances; and disorders of the speech system, including apraxia of speech and stuttering.Offering a bridge between the neurological and behavioral literatures on speech production, the book will be a valuable resource for researchers in both fields.
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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.
Price: 33.00 £ | Shipping*: 0.00 £
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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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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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