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Tag: convolutional neural networks

  • The Math of Neural Networks

    The Math of Neural Networks


    Price: $2.99
    (as of Dec 24,2024 07:07:31 UTC – Details)





    ASIN ‏ : ‎ B075H364NC
    Publisher ‏ : ‎ Blue Windmill Media (September 7, 2017)
    Publication date ‏ : ‎ September 7, 2017
    Language ‏ : ‎ English
    File size ‏ : ‎ 7507 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 187 pages

    Customers say

    Customers find the book informative and well-written. They say it provides basic information to start with and is a good stand-alone book. Readers also appreciate the exploration and teaching guide.

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    Neural networks are a powerful tool used in machine learning and artificial intelligence. But have you ever wondered about the math behind these complex systems? In this post, we will explore the mathematical principles that govern the inner workings of neural networks.

    At its core, a neural network is made up of interconnected nodes, or neurons, that work together to process and analyze data. Each neuron takes in input, applies a mathematical function to it, and produces an output. These outputs are then passed on to other neurons, creating a network of interconnected layers.

    The math behind neural networks involves a lot of linear algebra and calculus. For example, when data is passed through a neural network, it undergoes a series of matrix multiplications and activation functions. These operations help the network learn and make predictions based on the input data.

    One key concept in neural network math is the idea of gradients. Gradients represent the rate of change of a function, and they are used to update the weights and biases of a neural network during the training process. By adjusting these parameters based on the gradients, the network can learn to make better predictions and improve its performance.

    Overall, the math of neural networks is a fascinating and complex subject that plays a crucial role in the field of artificial intelligence. By understanding the mathematical principles behind these systems, we can gain a deeper insight into how neural networks work and how they can be optimized for various tasks.
    #Math #Neural #Networks

  • Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles

    Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles


    Price: $41.97
    (as of Dec 24,2024 06:20:55 UTC – Details)





    Publisher ‏ : ‎ Packt Publishing – ebooks Account (September 27, 2017)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 270 pages
    ISBN-10 ‏ : ‎ 1788397878
    ISBN-13 ‏ : ‎ 978-1788397872
    Item Weight ‏ : ‎ 1.06 pounds
    Dimensions ‏ : ‎ 9.25 x 7.52 x 0.57 inches



    Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles

    Neural networks have revolutionized the field of artificial intelligence, enabling machines to learn from data and make predictions in a way that mimics the human brain. In this post, we will explore how to implement neural networks in R using cutting-edge techniques such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for deep learning applications.

    CNNs are particularly well-suited for image recognition tasks, while RNNs excel at processing sequential data such as natural language text or time series data. By combining these powerful models with deep learning principles, we can create smart models that can solve a wide range of complex problems.

    In R, we can leverage popular libraries such as Keras and TensorFlow to build and train neural networks with ease. These libraries provide a high-level interface to define and train complex neural network architectures, making it accessible to both beginners and experienced practitioners.

    By understanding the underlying principles of artificial intelligence and neural networks, we can create sophisticated models that can learn from data, make predictions, and adapt to new information. With the right tools and techniques, we can unlock the full potential of neural networks and build intelligent systems that can revolutionize various industries.

    In conclusion, neural networks with R offer a powerful way to harness the potential of artificial intelligence and deep learning. By combining advanced techniques such as CNNs and RNNs with the flexibility of R programming, we can create smart models that can tackle complex problems and drive innovation in the field of AI.
    #Neural #Networks #Smart #models #CNN #RNN #deep #learning #artificial #intelligence #principles

  • Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models (Intelligent Computing, 1)

    Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models (Intelligent Computing, 1)


    Price: $164.99
    (as of Dec 24,2024 05:35:23 UTC – Details)





    Publisher ‏ : ‎ De Gruyter; 1st edition (August 6, 2024)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 444 pages
    ISBN-10 ‏ : ‎ 3111344002
    ISBN-13 ‏ : ‎ 978-3111344003
    Item Weight ‏ : ‎ 1.99 pounds
    Dimensions ‏ : ‎ 6.69 x 1 x 9.61 inches



    Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models (Intelligent Computing, 1)

    Artificial Intelligence (AI) has revolutionized the way we interact with technology, from voice assistants like Siri and Alexa to autonomous vehicles and smart homes. At the heart of AI are powerful algorithms that enable machines to learn from data and make intelligent decisions.

    Machine learning is a subfield of AI that focuses on developing algorithms that can learn from and make predictions or decisions based on data. One popular approach to machine learning is deep learning, which uses neural networks to model complex patterns in large datasets.

    Convolutional Neural Networks (CNNs) are a type of neural network that is particularly well-suited for analyzing visual data, such as images and videos. CNNs have been used in a wide range of applications, including image recognition, object detection, and medical imaging.

    Large language models, such as OpenAI’s GPT-3, have gained attention in recent years for their ability to generate human-like text. These models are trained on vast amounts of text data and can generate coherent and contextually relevant sentences.

    In this post, we will explore the key concepts and applications of machine learning, convolutional neural networks, and large language models in the field of artificial intelligence. Stay tuned for more insights on how these technologies are shaping the future of intelligent computing.
    #Artificial #Intelligence #Machine #Learning #Convolutional #Neural #Networks #Large #Language #Models #Intelligent #Computing

  • AI Foundations of Neural Networks: Easy To Read Guide Introducing the Foundations Of Neural Networks and AI

    AI Foundations of Neural Networks: Easy To Read Guide Introducing the Foundations Of Neural Networks and AI


    Price: $4.99
    (as of Dec 24,2024 04:49:55 UTC – Details)





    Are you curious about the world of artificial intelligence and neural networks, but find the technical jargon overwhelming? Look no further! In this easy-to-read guide, we will introduce you to the foundations of neural networks and AI, breaking down complex concepts into simple terms.

    Neural networks are a key component of artificial intelligence, mimicking the way the human brain processes information. They are made up of interconnected nodes, or “neurons,” that work together to process and analyze data. These networks can be trained to recognize patterns, make predictions, and even learn from experience.

    The key to understanding neural networks lies in their layers. Each layer consists of multiple neurons that perform specific tasks, such as inputting data, processing information, and producing an output. The connections between neurons are weighted, meaning some connections are stronger than others, influencing the network’s decisions.

    Training a neural network involves feeding it data and adjusting the weights of the connections to minimize errors in its predictions. This process, known as backpropagation, allows the network to learn from its mistakes and improve its performance over time.

    Artificial intelligence, on the other hand, refers to the broader field of creating intelligent machines that can think and act like humans. Neural networks are just one tool in the AI toolbox, but they have revolutionized fields such as image recognition, natural language processing, and self-driving cars.

    By understanding the foundations of neural networks and AI, you can gain insight into how these technologies work and how they are shaping our world. So, whether you’re a beginner looking to dip your toes into the world of AI or a seasoned professional seeking a refresher, this guide is for you. Stay tuned for more in-depth discussions on neural networks and AI in future posts!
    #Foundations #Neural #Networks #Easy #Read #Guide #Introducing #Foundations #Neural #Networks

  • A Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures

    A Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures



    A Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures

    Price : 39.44

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    Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, enabling machines to recognize patterns and objects in images with impressive accuracy. In this guide, we will provide a comprehensive overview of CNNs for computer vision, explaining their architecture, training process, and applications.

    1. Introduction to CNNs:
    – CNNs are a type of deep neural network designed specifically for processing visual data, such as images and videos.
    – They are composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers.
    – Convolutional layers apply filters to the input image, extracting features like edges, textures, and shapes.
    – Pooling layers reduce the spatial dimensions of the feature maps, helping to make the network more computationally efficient.

    2. Training CNNs:
    – CNNs are typically trained using a technique called backpropagation, where the network learns to adjust its parameters based on the error between the predicted and actual outputs.
    – Training data is fed into the network, and the weights of each layer are updated iteratively to minimize the loss function.
    – Common optimization algorithms used for training CNNs include Stochastic Gradient Descent (SGD), Adam, and RMSprop.

    3. Applications of CNNs in Computer Vision:
    – CNNs have been successfully applied to a wide range of computer vision tasks, including image classification, object detection, and image segmentation.
    – Image classification involves assigning a label to an input image based on its contents, such as identifying whether it contains a cat or a dog.
    – Object detection aims to locate and classify objects within an image, enabling tasks like autonomous driving and surveillance.
    – Image segmentation involves partitioning an image into different regions or objects, allowing for more detailed analysis and understanding.

    In conclusion, CNNs have become a powerful tool for computer vision, enabling machines to perceive and interpret visual data with human-like accuracy. By understanding the architecture, training process, and applications of CNNs, you can leverage this technology to develop innovative solutions in various domains.
    #Guide #Convolutional #Neural #Networks #Computer #Vision #Synthesis #Lectures

  • Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects

    Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects


    Price: $42.27
    (as of Dec 24,2024 04:02:27 UTC – Details)





    Publisher ‏ : ‎ Packt Publishing (February 28, 2019)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 308 pages
    ISBN-10 ‏ : ‎ 1789138906
    ISBN-13 ‏ : ‎ 978-1789138900
    Item Weight ‏ : ‎ 1.19 pounds
    Dimensions ‏ : ‎ 9.25 x 7.52 x 0.65 inches

    Customers say

    Customers find the book easy to understand and thorough. They appreciate the clear explanations of projects and topics, with detailed preprocessing sections. The content covers many topics in depth with good examples.

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    Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects

    Are you interested in diving into the world of neural networks and exploring their capabilities using Python? Look no further! In this post, we will guide you through six exciting projects that will help you harness the true power of neural networks.

    1. Handwritten Digit Recognition: Build a neural network model that can accurately recognize handwritten digits using the famous MNIST dataset. This project will help you understand the basics of image classification and how neural networks can be trained to recognize patterns in data.

    2. Sentiment Analysis: Develop a sentiment analysis model that can classify text as positive or negative. This project will show you how neural networks can be used for natural language processing tasks and how they can be trained on text data.

    3. Image Classification: Create a neural network model that can classify images into different categories. This project will demonstrate how neural networks can be used for more complex image recognition tasks and how they can be trained on large datasets like CIFAR-10.

    4. Stock Price Prediction: Build a neural network model that can predict stock prices based on historical data. This project will introduce you to the world of time series forecasting and show you how neural networks can be used to make accurate predictions.

    5. Fraud Detection: Develop a fraud detection model that can identify fraudulent transactions in a dataset. This project will showcase how neural networks can be used for anomaly detection tasks and how they can be trained on imbalanced datasets.

    6. Style Transfer: Create a neural network model that can transfer the style of one image onto another. This project will demonstrate how neural networks can be used for artistic tasks like image generation and style transfer.

    By working on these six projects, you will gain a deeper understanding of how neural networks work and how they can be applied to a wide range of tasks. So grab your Python IDE and start exploring the true power of neural networks today!
    #Neural #Network #Projects #Python #ultimate #guide #Python #explore #true #power #neural #networks #projects

  • Hands-on Convolutional Neural Networks with Tensorflow (Paperback or Softback)

    Hands-on Convolutional Neural Networks with Tensorflow (Paperback or Softback)



    Hands-on Convolutional Neural Networks with Tensorflow (Paperback or Softback)

    Price : 45.94 – 38.28

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    Are you looking to dive deeper into the world of Convolutional Neural Networks (CNNs) and learn how to implement them using TensorFlow? Look no further than “Hands-on Convolutional Neural Networks with TensorFlow”! This comprehensive guide is perfect for beginners and experienced professionals alike who want to explore the power of CNNs in machine learning and image recognition.

    In this book, you will learn the fundamentals of CNNs, how they work, and why they are so effective in tasks like image classification, object detection, and more. You will also get hands-on experience with building and training CNN models using TensorFlow, a popular deep learning framework.

    Whether you prefer a physical copy or a digital version, this book is available in both paperback and softback formats. So, grab your copy today and start mastering the art of Convolutional Neural Networks with TensorFlow!
    #Handson #Convolutional #Neural #Networks #Tensorflow #Paperback #Softback

  • Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vi – GOOD

    Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vi – GOOD



    Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vi – GOOD

    Price : 23.88

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    Are you interested in diving deeper into the world of computer vision and convolutional neural networks (CNNs)? Look no further than our latest workshop on Hands-On Convolutional Neural Networks with TensorFlow!

    In this workshop, you will learn how to build, train, and deploy CNNs using the popular deep learning framework TensorFlow. You will get hands-on experience working with real-world datasets and solving complex computer vision problems.

    Whether you are a beginner looking to learn the basics of CNNs or an experienced coder wanting to sharpen your skills, this workshop is for you. Join us and take your computer vision skills to the next level!

    Sign up now and start your journey towards mastering convolutional neural networks with TensorFlow. See you there! #computer vision #CNN #TensorFlow #deep learning
    #HandsOn #Convolutional #Neural #Networks #TensorFlow #Solve #computer #GOOD

  • Convolutional Neural Networks for Medical Applications (SpringerBriefs in Computer Science)

    Convolutional Neural Networks for Medical Applications (SpringerBriefs in Computer Science)


    Price: $54.99 – $7.29
    (as of Dec 24,2024 03:13:30 UTC – Details)





    Publisher ‏ : ‎ Springer; 1st ed. 2023 edition (March 24, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 108 pages
    ISBN-10 ‏ : ‎ 9811988137
    ISBN-13 ‏ : ‎ 978-9811988134
    Item Weight ‏ : ‎ 5.8 ounces
    Dimensions ‏ : ‎ 6.1 x 0.25 x 9.25 inches



    Convolutional Neural Networks (CNNs) have revolutionized the field of medical imaging and are being increasingly used for a variety of medical applications. In the book “Convolutional Neural Networks for Medical Applications” published by SpringerBriefs in Computer Science, researchers and practitioners explore the potential of CNNs in the healthcare industry.

    From diagnosing diseases to analyzing medical images, CNNs have shown promising results in various medical tasks. The book covers topics such as image classification, object detection, segmentation, and more, providing a comprehensive overview of how CNNs can be applied in the medical field.

    With contributions from experts in both computer science and healthcare, this book offers valuable insights into the latest developments and challenges in using CNNs for medical applications. Whether you are a researcher, practitioner, or student interested in the intersection of computer science and healthcare, this book is a must-read for anyone looking to stay updated on the latest trends in medical imaging and analysis.

    Get your copy of “Convolutional Neural Networks for Medical Applications” today and discover the potential of CNNs in revolutionizing healthcare.
    #Convolutional #Neural #Networks #Medical #Applications #SpringerBriefs #Computer #Science

  • Convolutional Neural Networks in Visual Computing: A Concise Guide

    Convolutional Neural Networks in Visual Computing: A Concise Guide



    Convolutional Neural Networks in Visual Computing: A Concise Guide

    Price : 60.00

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    Convolutional Neural Networks in Visual Computing: A Concise Guide

    In the world of visual computing, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for tasks such as image recognition, object detection, and image segmentation. CNNs have revolutionized the field of computer vision, enabling machines to see and understand the visual world in ways that were previously thought to be impossible.

    So, what exactly are CNNs and how do they work? In simple terms, CNNs are a type of deep learning algorithm that is inspired by the way the human brain processes visual information. They consist of multiple layers of neurons that are organized in a hierarchical manner, with each layer responsible for extracting different features from the input image.

    The key component of a CNN is the convolutional layer, which applies a series of filters to the input image in order to extract features such as edges, textures, and shapes. These features are then passed through additional layers such as pooling and fully connected layers, which help to further refine the extracted features and make predictions about the content of the input image.

    One of the main advantages of CNNs is their ability to learn features directly from the data, without the need for manual feature engineering. This means that CNNs are able to adapt to a wide range of visual tasks and can achieve state-of-the-art performance on tasks such as image classification, object detection, and image segmentation.

    In conclusion, Convolutional Neural Networks have revolutionized the field of visual computing and are now a key tool in the development of intelligent systems that are able to see and understand the visual world. By understanding the basics of CNNs and how they work, you can begin to explore the exciting possibilities that this technology has to offer in the realm of visual computing.
    #Convolutional #Neural #Networks #Visual #Computing #Concise #Guide