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Tag: neural networks

  • Neural Networks (In Search of Media)

    Neural Networks (In Search of Media)


    Price: $7.99
    (as of Dec 24,2024 11:28:32 UTC – Details)




    ASIN ‏ : ‎ B0C2GSQQWF
    Publisher ‏ : ‎ Univ Of Minnesota Press (April 9, 2024)
    Publication date ‏ : ‎ April 9, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 2270 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Enabled
    Print length ‏ : ‎ 124 pages
    Page numbers source ISBN ‏ : ‎ 1517916690


    Neural Networks (In Search of Media)

    Neural networks have revolutionized the field of artificial intelligence and machine learning, enabling computers to learn from data and make decisions in a way that mimics the human brain. As these complex systems become more prevalent in various industries, there is a growing need for high-quality media that can help explain and demystify the inner workings of neural networks.

    From informative articles and videos to interactive tutorials and podcasts, there is a vast array of media that can help both beginners and experts understand the intricacies of neural networks. Whether you are a student looking to grasp the fundamentals or a professional seeking to stay up-to-date on the latest advancements, finding reliable and engaging media is crucial.

    In this post, we will explore different types of media that can serve as valuable resources for those interested in neural networks. Stay tuned for recommendations, reviews, and tips on where to find the best content to expand your knowledge and enhance your skills in the fascinating world of neural networks.
    #Neural #Networks #Search #Media

  • Introduction to the Math of Neural Networks

    Introduction to the Math of Neural Networks


    Price: $1.99
    (as of Dec 24,2024 10:44:09 UTC – Details)




    ASIN ‏ : ‎ B00845UQL6
    Publisher ‏ : ‎ Heaton Research, Inc. (April 3, 2012)
    Publication date ‏ : ‎ April 3, 2012
    Language ‏ : ‎ English
    File size ‏ : ‎ 912 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 122 pages

    Customers say

    Customers find the book’s introduction to math thorough and informative. They find it easy to read, well-written, and interesting. Many consider it a good value for the price.

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    Neural networks have become increasingly popular in the field of artificial intelligence, with applications ranging from image and speech recognition to natural language processing. But how exactly do these complex systems work? In this post, we will provide an introduction to the math behind neural networks.

    At its core, a neural network is a collection of interconnected nodes, or neurons, that work together to process and analyze data. Each neuron takes in input, applies a mathematical operation to it, and outputs a result. These operations are typically linear transformations followed by non-linear activation functions, which introduce non-linearity into the network.

    The basic building block of a neural network is the perceptron, which consists of a single neuron. The input to the perceptron is multiplied by a set of weights, summed together with a bias term, and passed through an activation function to produce the output. The weights and bias are parameters that are learned during the training process, where the network adjusts them to minimize the error between the predicted and actual outputs.

    As neural networks become deeper and more complex, the math behind them becomes more intricate. Deep learning models often consist of multiple layers of neurons, each connected to the next in a hierarchical fashion. The training process involves adjusting the weights and biases of all neurons in the network using techniques like gradient descent and backpropagation.

    Understanding the math behind neural networks is crucial for building and training effective models. By grasping concepts like linear transformations, activation functions, and optimization algorithms, you can better comprehend how these powerful systems operate. In future posts, we will delve deeper into specific mathematical concepts and techniques used in neural networks. Stay tuned!
    #Introduction #Math #Neural #Networks

  • Neural Networks: A Systematic Introduction

    Neural Networks: A Systematic Introduction


    Price: $99.99 – $80.66
    (as of Dec 24,2024 09:57:35 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st edition (July 12, 1996)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 522 pages
    ISBN-10 ‏ : ‎ 3540605053
    ISBN-13 ‏ : ‎ 978-3540605058
    Item Weight ‏ : ‎ 1.55 pounds
    Dimensions ‏ : ‎ 6.1 x 1.19 x 9.25 inches


    Neural Networks: A Systematic Introduction

    Neural networks have become an integral part of modern technology, powering everything from autonomous vehicles to virtual assistants. But what exactly are neural networks, and how do they work? In this post, we will provide a systematic introduction to neural networks, breaking down the complex concepts into easy-to-understand terms.

    What are Neural Networks?

    Neural networks are a type of artificial intelligence that is inspired by the structure of the human brain. They consist of interconnected nodes, or “neurons,” that work together to process and analyze data. These networks are capable of learning from data and making predictions or decisions based on that data.

    How do Neural Networks Work?

    At the core of a neural network is the neuron, which takes in input data, processes it using a set of weights and biases, and produces an output. These neurons are organized into layers, with each layer performing a specific task in the overall computation.

    The first layer of a neural network is the input layer, which receives the initial data. The data is then passed through one or more hidden layers, where the neurons perform complex computations on the data. Finally, the output layer produces the final result of the neural network’s computation.

    Training a Neural Network

    One of the key features of neural networks is their ability to learn from data. This process, known as training, involves adjusting the weights and biases of the neurons in the network to minimize the error between the predicted output and the actual output.

    During training, the network is fed a set of labeled data, with the desired output for each input. The network then adjusts its weights and biases using a process called backpropagation, which involves calculating the gradient of the error function and updating the weights in the opposite direction of the gradient.

    Applications of Neural Networks

    Neural networks have a wide range of applications in fields such as image and speech recognition, natural language processing, and financial forecasting. They are also used in autonomous systems, such as self-driving cars and drones, where they can make decisions based on real-time data.

    In conclusion, neural networks are a powerful tool for processing and analyzing data, with the ability to learn and adapt to new information. By understanding the basic principles of neural networks, we can unlock their full potential in solving complex problems and advancing technology.
    #Neural #Networks #Systematic #Introduction

  • Building Neural Networks from Scratch with Python

    Building Neural Networks from Scratch with Python


    Price: $24.95
    (as of Dec 24,2024 09:10:58 UTC – Details)


    From the Publisher

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    Neural Networks in python neural networks and deep learning a textbook

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    introduction to neural networks recurrent neural networks physics informed neural networks

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    Neural Networks in python neural networks and deep learning a textbook

    ASIN ‏ : ‎ B0CQKNMJZK
    Publication date ‏ : ‎ December 17, 2023
    Language ‏ : ‎ English
    File size ‏ : ‎ 454 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 178 pages
    Page numbers source ISBN ‏ : ‎ B0CQXL4GFG


    Building Neural Networks from Scratch with Python

    In this post, we will explore how to create neural networks from scratch using Python. Neural networks are a powerful tool in machine learning and can be used for tasks such as image recognition, natural language processing, and more. By understanding the fundamentals of neural networks and implementing them in Python, you can gain a deeper insight into how they work and how to customize them for your specific needs.

    To build a neural network from scratch, we will cover the following steps:

    1. Define the structure of the neural network, including the number of layers, the number of nodes in each layer, and the activation functions.
    2. Initialize the weights and biases for each layer.
    3. Implement the forward propagation algorithm to pass input data through the network and calculate the output.
    4. Define a loss function to measure the error between the predicted output and the actual output.
    5. Implement the backpropagation algorithm to update the weights and biases based on the error calculated by the loss function.
    6. Train the neural network by repeating the forward and backward passes with a training dataset to minimize the loss function.

    By following these steps and implementing them in Python, you can create a fully functional neural network that can be used for a variety of machine learning tasks. Stay tuned for our upcoming tutorials on building and training neural networks from scratch!
    #Building #Neural #Networks #Scratch #Python

  • Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))

    Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))


    Price: $115.00 – $49.60
    (as of Dec 24,2024 08:26:39 UTC – Details)




    ASIN ‏ : ‎ 0198538642
    Publisher ‏ : ‎ Oxford University Press, USA; 1st edition (January 18, 1996)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 502 pages
    ISBN-10 ‏ : ‎ 9780198538646
    ISBN-13 ‏ : ‎ 978-0198538646
    Item Weight ‏ : ‎ 1.65 pounds
    Dimensions ‏ : ‎ 1.12 x 9.19 x 6.19 inches


    In this post, we will delve into the fascinating world of neural networks for pattern recognition, specifically focusing on the advanced concepts discussed in the book “Neural Networks for Pattern Recognition” by Christopher M. Bishop.

    Neural networks have revolutionized the field of pattern recognition, allowing machines to learn complex patterns and make intelligent decisions based on data. This book provides a comprehensive overview of neural network theory and applications in the field of econometrics.

    Readers will gain a deep understanding of the mathematical foundations of neural networks, including topics such as feedforward networks, recurrent networks, and deep learning architectures. The book also covers advanced topics such as Bayesian neural networks, Gaussian processes, and variational inference.

    By studying the concepts presented in this book, readers will be equipped with the knowledge and skills needed to apply neural networks to real-world problems in econometrics, finance, and other fields. Whether you are a student, researcher, or practitioner, “Neural Networks for Pattern Recognition” is a valuable resource for anyone interested in harnessing the power of neural networks for pattern recognition.
    #Neural #Networks #Pattern #Recognition #Advanced #Texts #Econometrics #Paperback

  • Neural Networks for Engineers: A Mathematical Treatise From Fundamentals to Advanced Deep Learning Techniques (Data Sciences)

    Neural Networks for Engineers: A Mathematical Treatise From Fundamentals to Advanced Deep Learning Techniques (Data Sciences)


    Price: $60.00
    (as of Dec 24,2024 07:41:04 UTC – Details)



    Neural Networks for Engineers: A Mathematical Treatise From Fundamentals to Advanced Deep Learning Techniques (Data Sciences)

    In the world of data sciences, neural networks have become a powerful tool for engineers to analyze and extract valuable insights from large datasets. From image recognition to natural language processing, neural networks have revolutionized the way we approach complex problems in various fields.

    This post aims to provide engineers with a comprehensive overview of neural networks, starting from the fundamentals and progressing to advanced deep learning techniques. We will delve into the mathematical principles behind neural networks, exploring topics such as activation functions, backpropagation, and optimization algorithms.

    We will also discuss the various types of neural networks, including feedforward networks, convolutional neural networks, and recurrent neural networks, and examine how they can be applied to different real-world applications.

    Lastly, we will explore cutting-edge developments in the field of deep learning, such as generative adversarial networks (GANs), reinforcement learning, and transfer learning. By the end of this post, engineers will have a solid understanding of neural networks and be equipped with the knowledge to apply them effectively in their own projects.

    Whether you are a beginner looking to learn the basics of neural networks or an experienced engineer seeking to deepen your understanding of advanced deep learning techniques, this post is sure to provide valuable insights and practical knowledge to help you succeed in the exciting field of data sciences. Stay tuned for more updates on Neural Networks for Engineers!
    #Neural #Networks #Engineers #Mathematical #Treatise #Fundamentals #Advanced #Deep #Learning #Techniques #Data #Sciences

  • The Self-Assembling Brain: How Neural Networks Grow Smarter

    The Self-Assembling Brain: How Neural Networks Grow Smarter


    Price: $0.00
    (as of Dec 24,2024 06:53:26 UTC – Details)


    Customers say

    Customers find the book profound, fascinating, and educational. They appreciate the various narrative approaches used to introduce the content, including an attention-grabbing prologue that gives them goosebumps. The book is described as scholarly yet accessible, with a fun read.

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    The human brain is a complex and fascinating organ that continues to baffle scientists with its ability to learn, adapt, and grow smarter over time. One of the key mechanisms behind this phenomenon is the self-assembling nature of neural networks within the brain.

    Neural networks are interconnected networks of neurons that communicate with each other through electrical and chemical signals. These networks form the basis of our cognitive functions, such as memory, learning, and decision-making.

    When we learn something new or experience something, our brain creates new connections between neurons, strengthening existing connections and forming new ones. This process, known as synaptic plasticity, allows our brain to adapt and change in response to new information or experiences.

    As we continue to learn and experience new things, our neural networks grow and reorganize themselves to become more efficient and effective. This process is known as neuroplasticity, and it plays a crucial role in our ability to learn new skills, solve problems, and make decisions.

    Through neuroplasticity, our brain is constantly evolving and adapting to its environment, allowing us to continually improve our cognitive abilities and grow smarter over time. By understanding and harnessing the self-assembling nature of neural networks, we can unlock the full potential of our brains and enhance our cognitive abilities in ways we never thought possible.
    #SelfAssembling #Brain #Neural #Networks #Grow #Smarter

  • Graph Neural Networks: Foundations, Frontiers, and Applications

    Graph Neural Networks: Foundations, Frontiers, and Applications


    Price: $119.99 – $55.78
    (as of Dec 24,2024 06:06:53 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2022 edition (January 4, 2022)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 725 pages
    ISBN-10 ‏ : ‎ 9811660530
    ISBN-13 ‏ : ‎ 978-9811660535
    Item Weight ‏ : ‎ 2.62 pounds
    Dimensions ‏ : ‎ 6.25 x 1.75 x 9.5 inches


    Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling complex relationships in data. In this post, we will explore the foundations of GNNs, discuss current frontiers in the field, and highlight some of the exciting applications where GNNs have been successfully applied.

    Foundations of Graph Neural Networks:
    GNNs are a type of neural network that operate on graph-structured data, such as social networks, citation networks, and molecular structures. Unlike traditional neural networks, which operate on grid-structured data like images or text, GNNs are able to capture the relational structure of data and leverage this information to make predictions.

    At the core of GNNs are message passing algorithms, which allow nodes in a graph to exchange information with their neighbors. By iteratively passing messages between nodes, GNNs are able to aggregate information from the entire graph and make predictions based on this global context.

    Frontiers in Graph Neural Networks:
    One of the key challenges in GNN research is developing models that are able to effectively capture long-range dependencies in graphs. Current research is focused on designing more powerful message passing algorithms, incorporating attention mechanisms, and learning hierarchical representations of graph data.

    Another frontier in GNN research is developing models that are able to handle dynamic graphs, where the structure of the graph changes over time. This requires designing GNNs that are able to adapt to changes in the graph topology and learn from sequential data.

    Applications of Graph Neural Networks:
    GNNs have been successfully applied to a wide range of applications, including social network analysis, recommendation systems, drug discovery, and computer vision. In social network analysis, GNNs can be used to predict user behavior, detect communities, and identify influential nodes. In drug discovery, GNNs have been used to predict the properties of molecules and design new drugs. In computer vision, GNNs have been applied to tasks like image segmentation, object detection, and image generation.

    Overall, Graph Neural Networks have become a powerful tool for analyzing and modeling complex relationships in data. With ongoing research pushing the boundaries of what is possible with GNNs, we can expect to see even more exciting applications of this technology in the future.
    #Graph #Neural #Networks #Foundations #Frontiers #Applications

  • Hands-On Graph Neural Networks using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

    Hands-On Graph Neural Networks using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch


    Price: $47.49
    (as of Dec 24,2024 05:21:19 UTC – Details)


    From the brand

    Brand story Packt books

    Brand story Packt books

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    Packt Logo

    Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

    Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

    We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

    Publisher ‏ : ‎ Packt Publishing – ebooks Account (May 9, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 365 pages
    ISBN-10 ‏ : ‎ 1804617520
    ISBN-13 ‏ : ‎ 978-1804617526
    Item Weight ‏ : ‎ 1.36 pounds
    Dimensions ‏ : ‎ 9.25 x 7.52 x 0.74 inches


    Graph Neural Networks (GNNs) have gained popularity in recent years for their ability to model complex relationships and dependencies in data that can be represented as graphs. In this post, we will explore hands-on techniques and architectures for building powerful graph and deep learning applications using Python and PyTorch.

    We will start by introducing the basics of graph theory and how it can be applied to machine learning tasks. Then, we will dive into the fundamentals of GNNs, including how they work, different types of GNN architectures, and how they can be implemented in PyTorch.

    Throughout the post, we will walk through practical examples and code snippets to help you understand how to build and train GNN models using PyTorch. We will cover topics such as graph representation, message passing, node classification, link prediction, and more.

    By the end of this post, you will have a solid understanding of how to leverage GNNs for your own projects and how to use PyTorch to implement powerful graph and deep learning applications. Stay tuned for more updates on Hands-On Graph Neural Networks using Python!
    #HandsOn #Graph #Neural #Networks #Python #Practical #techniques #architectures #building #powerful #graph #deep #learning #apps #PyTorch

  • Build Your Own Neural Networks: Step-By-Step Explanation For Beginners

    Build Your Own Neural Networks: Step-By-Step Explanation For Beginners


    Price: $14.99
    (as of Dec 24,2024 04:35:52 UTC – Details)




    ASIN ‏ : ‎ B0D7TNTBHW
    Publisher ‏ : ‎ Independently published (June 23, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 169 pages
    ISBN-13 ‏ : ‎ 979-8329218473
    Item Weight ‏ : ‎ 1.12 pounds
    Dimensions ‏ : ‎ 8.5 x 0.39 x 11 inches


    Are you interested in learning how to build your own neural networks but don’t know where to start? Look no further! In this post, we’ll provide a step-by-step explanation for beginners on how to build your own neural networks.

    Step 1: Understand the Basics of Neural Networks
    Before diving into building your own neural networks, it’s important to have a basic understanding of what neural networks are and how they work. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labelling or clustering raw input.

    Step 2: Choose a Platform or Framework
    There are several platforms and frameworks available for building neural networks, such as TensorFlow, PyTorch, and Keras. Choose the one that best suits your needs and comfort level. For beginners, we recommend starting with TensorFlow as it offers a user-friendly interface and extensive documentation.

    Step 3: Define Your Neural Network Architecture
    Next, you’ll need to define the architecture of your neural network. This includes determining the number of layers, the number of neurons in each layer, and the activation functions to use. Start with a simple architecture, such as a feedforward neural network with one hidden layer, before moving on to more complex architectures.

    Step 4: Train Your Neural Network
    Once you’ve defined your neural network architecture, it’s time to train your model on a dataset. This involves feeding the model input data and adjusting the weights and biases of the neurons to minimize the error between the predicted output and the actual output. You can use gradient descent or other optimization algorithms to update the weights and biases.

    Step 5: Evaluate Your Model
    After training your neural network, it’s important to evaluate its performance on a test dataset. This will help you determine how well your model is able to generalize to new, unseen data. You can use metrics such as accuracy, precision, recall, and F1 score to evaluate the performance of your model.

    By following these steps, you’ll be well on your way to building your own neural networks. Remember, practice makes perfect, so don’t be discouraged if you encounter challenges along the way. Keep experimenting and learning, and you’ll soon be able to create powerful neural networks for a variety of applications.
    #Build #Neural #Networks #StepByStep #Explanation #Beginners

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