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Tag: Classifying

  • Convolutional Neural Network in classifying scanned documents

    Convolutional Neural Network in classifying scanned documents


    Price: $42.90 – $3.85
    (as of Dec 26,2024 13:53:33 UTC – Details)




    Publisher ‏ : ‎ Grin Verlag (January 5, 2017)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 40 pages
    ISBN-10 ‏ : ‎ 3668371687
    ISBN-13 ‏ : ‎ 978-3668371682
    Item Weight ‏ : ‎ 2.34 ounces
    Dimensions ‏ : ‎ 5.83 x 0.1 x 8.27 inches


    Convolutional Neural Networks (CNNs) have revolutionized the field of image recognition and classification, but did you know they can also be highly effective in classifying scanned documents?

    Scanned documents often pose a challenge for traditional machine learning algorithms due to variations in image quality, orientation, and text size. However, CNNs are adept at learning hierarchical features from raw pixel data, making them well-suited for the task of document classification.

    By leveraging the spatial relationships between pixels, CNNs can automatically extract relevant features from scanned documents, such as text, logos, signatures, and other visual elements. This allows them to accurately categorize documents into different classes, such as invoices, receipts, contracts, and more.

    Furthermore, CNNs can be trained on large datasets of labeled scanned documents to learn complex patterns and variations in document layouts. This enables them to generalize well to unseen documents and achieve high levels of accuracy in classification tasks.

    In summary, CNNs offer a powerful and efficient solution for classifying scanned documents, making them an invaluable tool for document processing and information retrieval tasks. If you’re interested in learning more about how CNNs can be applied to document classification, stay tuned for upcoming posts on this topic!
    #Convolutional #Neural #Network #classifying #scanned #documents

  • Learning Machines: Foundations of Trainable Pattern Classifying Systems Nilsson

    Learning Machines: Foundations of Trainable Pattern Classifying Systems Nilsson



    Learning Machines: Foundations of Trainable Pattern Classifying Systems Nilsson

    Price : 28.00

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    Learning Machines: Foundations of Trainable Pattern Classifying Systems by Nils J. Nilsson

    In this groundbreaking book, Nils J. Nilsson explores the foundations of trainable pattern classifying systems, also known as learning machines. Drawing on his extensive research and expertise in artificial intelligence, Nilsson delves into the theory and practical applications of these systems, shedding light on how they can be used to solve complex problems in a wide range of fields.

    From the basics of pattern recognition to advanced machine learning algorithms, Nilsson covers it all in this comprehensive guide. Whether you’re a student, researcher, or practitioner in the field of artificial intelligence, this book is a must-read for anyone interested in understanding the inner workings of trainable pattern classifying systems.

    With clear explanations, detailed examples, and insightful commentary, Learning Machines is a valuable resource for anyone looking to dive deep into the world of artificial intelligence and machine learning. Nilsson’s expertise and passion for the subject shine through in every chapter, making this book a must-have for anyone interested in the future of intelligent systems.
    #Learning #Machines #Foundations #Trainable #Pattern #Classifying #Systems #Nilsson,machine learning

  • Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

    Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning


    Price: $9.95
    (as of Dec 24,2024 15:21:54 UTC – Details)




    ASIN ‏ : ‎ B07NKT94GV
    Publisher ‏ : ‎ M.J. Magic Publishing; 1st edition (February 9, 2019)
    Publication date ‏ : ‎ February 9, 2019
    Language ‏ : ‎ English
    File size ‏ : ‎ 4207 KB
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Format ‏ : ‎ Print Replica


    Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

    In this post, we will explore the process of image classification using Python and popular techniques of computer vision and machine learning. Image classification is the task of assigning a label or category to an image based on its content. This task is commonly used in various fields such as healthcare, autonomous driving, and security.

    To classify images, we will be using the following steps:

    1. Data Collection: The first step in image classification is to gather a dataset of images that are labeled with their corresponding categories. This dataset will be used to train our machine learning model.

    2. Data Preprocessing: Before feeding the images into the model, we need to preprocess them by resizing, normalizing, and augmenting the data to improve the model’s performance.

    3. Model Building: We will build a convolutional neural network (CNN) using popular libraries such as TensorFlow or PyTorch. CNNs are widely used in image classification tasks due to their ability to learn spatial hierarchies of features.

    4. Model Training: We will train the CNN model on our dataset using techniques such as backpropagation and stochastic gradient descent to optimize the model’s parameters.

    5. Model Evaluation: Once the model is trained, we will evaluate its performance on a separate test dataset to measure its accuracy, precision, recall, and F1 score.

    6. Prediction: Finally, we will use the trained model to classify new images and make predictions on their categories.

    By following these steps and utilizing the power of computer vision and machine learning techniques, we can successfully classify images with Python. Stay tuned for the upcoming tutorials where we will dive deeper into each of these steps and provide code examples to help you get started with image classification.
    #Image #Classification #Stepbystep #Classifying #Images #Python #Techniques #Computer #Vision #Machine #Learning

  • Image Classification Using Python and Techniques of Computer Vision and Machine Learning: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

    Image Classification Using Python and Techniques of Computer Vision and Machine Learning: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning


    Price: $9.95
    (as of Dec 17,2024 09:22:58 UTC – Details)




    ASIN ‏ : ‎ B09GNLRSZX
    Publisher ‏ : ‎ M.J. Magic Publishing; 2nd edition (November 11, 2021)
    Publication date ‏ : ‎ November 11, 2021
    Language ‏ : ‎ English
    File size ‏ : ‎ 6286 KB
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Format ‏ : ‎ Print Replica


    In this post, we will explore the process of image classification using Python and techniques of computer vision and machine learning. Image classification is the task of assigning a label to an image based on its content. This can be a challenging problem, especially when dealing with large datasets and complex images.

    To get started, we will first need to install the necessary libraries for working with images and machine learning in Python. We will be using the following libraries:

    1. NumPy: A library for working with arrays and matrices in Python.
    2. OpenCV: An open-source computer vision library for image processing and analysis.
    3. Scikit-learn: A machine learning library for Python.

    Once we have installed these libraries, we can begin the process of classifying images. The steps involved in image classification using Python and techniques of computer vision and machine learning are as follows:

    1. Load the dataset: We will start by loading a dataset of images that we want to classify. This dataset can be downloaded from a variety of sources, such as Kaggle or the MNIST dataset.

    2. Preprocess the images: Before we can classify the images, we need to preprocess them by resizing, normalizing, and converting them to grayscale or RGB format.

    3. Feature extraction: Next, we will extract features from the images using techniques such as edge detection, corner detection, or histogram of oriented gradients (HOG).

    4. Train a machine learning model: We will then train a machine learning model, such as a support vector machine (SVM) or a convolutional neural network (CNN), on the extracted features.

    5. Evaluate the model: Finally, we will evaluate the performance of the model on a test set of images and calculate metrics such as accuracy, precision, and recall.

    By following these steps, we can successfully classify images using Python and techniques of computer vision and machine learning. This process can be applied to a wide range of image classification tasks, from recognizing handwritten digits to identifying objects in photographs.

    In conclusion, image classification is a powerful technique that can be used in a variety of applications, such as medical imaging, autonomous driving, and facial recognition. By leveraging the capabilities of Python and techniques of computer vision and machine learning, we can build accurate and efficient image classification models.
    #Image #Classification #Python #Techniques #Computer #Vision #Machine #Learning #Stepbystep #Classifying #Images #Python #Techniques #Computer #Vision #Machine #Learning

  • Learning Machines: Foundations of Trainable Pattern Classifying Systems Nilsson

    Learning Machines: Foundations of Trainable Pattern Classifying Systems Nilsson



    Learning Machines: Foundations of Trainable Pattern Classifying Systems Nilsson

    Price : 30.00

    Ends on : N/A

    View on eBay
    Learning Machines: Foundations of Trainable Pattern Classifying Systems by Nils Nilsson

    In this groundbreaking book, Nils Nilsson explores the foundations of trainable pattern classifying systems, also known as learning machines. Nilsson delves into the principles and algorithms that underlie these systems, providing a comprehensive overview of the field.

    From neural networks to decision trees, Nilsson covers a wide range of topics related to pattern classification and machine learning. He discusses the importance of data representation, feature selection, and model evaluation in designing effective learning systems.

    Whether you’re a student, researcher, or practitioner in the field of machine learning, this book is a must-read. Nilsson’s clear and concise writing style makes complex concepts accessible to readers of all backgrounds.

    If you’re looking to deepen your understanding of trainable pattern classifying systems, pick up a copy of Learning Machines: Foundations of Trainable Pattern Classifying Systems today. You won’t be disappointed.
    #Learning #Machines #Foundations #Trainable #Pattern #Classifying #Systems #Nilsson

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