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    Image Processing Masterclass with Python Sandipan Dey

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Image Processing Masterclass with Python Sandipan Dey - okladka książki

    Image Processing Masterclass with Python Sandipan Dey - okladka książki

    Image Processing Masterclass with Python Sandipan Dey - audiobook MP3

    Image Processing Masterclass with Python Sandipan Dey - audiobook CD

    Autor:
    Sandipan Dey
    Wydawnictwo:
    BPB Publications (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    428
    Dostępne formaty:
         ePub
         Mobi
    Over 50 problems solved with classical algorithms + ML / DL models

    Key Features
  1. Problem-driven approach to practice image processing.
  2. Practical usage of popular Python libraries: Numpy, Scipy, scikit-image, PIL and SimpleITK.
  3. End-to-end demonstration of popular facial image processing challenges using MTCNN and Microsofts Cognitive Vision APIs.

    Description
    This book starts with basic Image Processing and manipulation problems and demonstrates how to solve them with popular Python libraries and modules. It then concentrates on problems based on Geometric image transformations and problems to be solved with Image hashing.

    Next, the book focuses on solving problems based on Sampling, Convolution, Discrete Fourier transform, Frequency domain filtering and image restoration with deconvolution. It also aims at solving Image enhancement problems using different algorithms such as spatial filters and create a super resolution image using SRGAN.

    Finally, it explores popular facial image processing problems and solves them with Machine learning and Deep learning models using popular python ML / DL libraries.

    What you will learn
  4. Develop strong grip on the fundamentals of Image Processing and Image Manipulation.
  5. Solve popular Image Processing problems using Machine Learning and Deep Learning models.
  6. Working knowledge on Python libraries including numpy, scipy and scikit-image.
  7. Use popular Python Machine Learning packages such as scikit-learn, Keras and pytorch.
  8. Live implementation of Facial Image Processing techniques such as Face Detection / Recognition / Parsing dlib and MTCNN.

    Who this book is for
    This book is designed specially for computer vision users, machine learning engineers, image processing experts who are looking for solving modern image processing/computer vision challenges.

    Table of Contents
    1. Chapter 1: Basic Image & Video Processing
    2. Chapter 2: More Image Transformation and Manipulation
    3. Chapter 3: Sampling, Convolution and Discrete Fourier Transform
    4. Chapter 4: Discrete Cosine / Wavelet Transform and Deconvolution
    5. Chapter 5: Image Enhancement
    6. Chapter 6: More Image Enhancement
    7. Chapter 7: Face Image Processing

    About the Author
    Sandipan Dey is a Data Scientist with a wide range of interests, covering topics such as Machine Learning, Deep Learning, Image Processing and Computer Vision. He has worked in numerous data science fields, such as recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in Computer Science from the University of Maryland, Baltimore County, and has published in a few IEEE data mining conferences and journals. He has also authored a couple of Image Processing books, published from an international publication house. He has earned certifications from 100+ MOOCs on data science and related courses. He is a regular blogger (at sandipanweb @wordpress, medium and data science central) and is a Machine Learning education enthusiast.

    LinkedIn Profile: https://www.linkedin.com/in/sandipan-dey-0370276

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    O autorze książki

    Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.

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