Python Image Processing Cookbook

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Python Image Processing Cookbook

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Author : Sandipan Dey
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-17

Python Image Processing Cookbook written by Sandipan Dey and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-17 with Computers categories.

Explore Keras, scikit-image, open source computer vision (OpenCV), Matplotlib, and a wide range of other Python tools and frameworks to solve real-world image processing problems Key FeaturesDiscover solutions to complex image processing tasks using Python tools such as scikit-image and KerasLearn popular concepts such as machine learning, deep learning, and neural networks for image processingExplore common and not-so-common challenges faced in image processingBook Description With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively. What you will learnImplement supervised and unsupervised machine learning algorithms for image processingUse deep neural network models for advanced image processing tasksPerform image classification, object detection, and face recognitionApply image segmentation and registration techniques on medical images to assist doctorsUse classical image processing and deep learning methods for image restorationImplement text detection in images using Tesseract, the optical character recognition (OCR) engineUnderstand image enhancement techniques such as gradient blendingWho this book is for This book is for image processing engineers, computer vision engineers, software developers, machine learning engineers, or anyone who wants to become well-versed with image processing techniques and methods using a recipe-based approach. Although no image processing knowledge is expected, prior Python coding experience is necessary to understand key concepts covered in the book.

Opencv 3 Computer Vision With Python Cookbook

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Author : Aleksei Spizhevoi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-23

Opencv 3 Computer Vision With Python Cookbook written by Aleksei Spizhevoi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-23 with Computers categories.

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems ...

Machine Learning With Python Cookbook

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Author : Kyle Gallatin
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-27

Machine Learning With Python Cookbook written by Kyle Gallatin and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-27 with Computers categories.

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naäve Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks

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Hands On Image Processing With Python

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Author : Sandipan Dey
language : en
Publisher:
Release Date : 2018-11-30

Hands On Image Processing With Python written by Sandipan Dey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.

Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks. Key Features Practical coverage of every image processing task with popular Python libraries Includes topics such as pseudo-coloring, noise smoothing, computing image descriptors Covers popular machine learning and deep learning techniques for complex image processing tasks Book Description Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing. What you will learn Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python Do morphological image processing and segment images with different algorithms Learn techniques to extract features from images and match images Write Python code to implement supervised / unsupervised machine learning algorithms for image processing Use deep learning models for image classification, segmentation, object detection and style transfer Who this book is for This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

The Image Processing Cookbook

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Author : John C. Russ
language : en
Publisher: CreateSpace
Release Date : 2011-01-14

The Image Processing Cookbook written by John C. Russ and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-14 with Science categories.

The book leads the reader through a practical step-by-step workflow for processing images to correct defects and enhance the details for maximum visibility and presentation, and to isolate features for measurement of structural, object or geometric information in the images, using Adobe Photoshop with free and commercially available plugins. Intended for college students, forensic technicians, research scientists, and others who rely upon images as a source of information, the text includes procedures appropriate for light and electron microscopy, crime scene photographs, surveillance video, and other applications. The author is a retired university professor who has taught thousands of students these methods in a 50-year career, has written several widely used reference and textbooks on the subject, and is frequently called upon as an expert witness. This text is an outgrowth of his teaching, and uses a rich set of example images to illustrate the various algorithms that are described and applied.

Natural Language Processing With Python Cookbook

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Author : Krishna Bhavsar
language : en
Publisher:
Release Date : 2017-11-24

Natural Language Processing With Python Cookbook written by Krishna Bhavsar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-24 with Computers categories.

Learn the tricks and tips that will help you design Text Analytics solutionsAbout This Book* Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python* Use dictionaries to create your own named entities using this easy-to-follow guide* Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language ProcessingWho This Book Is ForThis book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended.What You Will Learn* Explore corpus management using internal and external corpora* Learn WordNet usage and a couple of simple application assignments using WordNet* Operate on raw text* Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more* Understand regular expressions for pattern matching* Learn to use and write your own POS taggers and grammars* Learn to evaluate your own trained models* Explore Deep Learning techniques in NLP* Generate Text from Nietzsche's writing using LSTM* Utilize the BABI dataset and LSTM to model episodesIn DetailNatural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora.This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK-the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques.By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python.Style and ApproachThis book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.

Python Machine Learning Cookbook

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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-30

Python Machine Learning Cookbook written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-30 with Computers categories.

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key FeaturesLearn and implement machine learning algorithms in a variety of real-life scenariosCover a range of tasks catering to supervised, unsupervised and reinforcement learning techniquesFind easy-to-follow code solutions for tackling common and not-so-common challengesBook Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learnUse predictive modeling and apply it to real-world problemsExplore data visualization techniques to interact with your dataLearn how to build a recommendation engineUnderstand how to interact with text data and build models to analyze itWork with speech data and recognize spoken words using Hidden Markov ModelsGet well versed with reinforcement learning, automated ML, and transfer learningWork with image data and build systems for image recognition and biometric face recognitionUse deep neural networks to build an optical character recognition systemWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

Python Deep Learning Cookbook

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Author : Indra den Bakker
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-10-27

Python Deep Learning Cookbook written by Indra den Bakker and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-27 with Computers categories.

Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide About This Book Practical recipes on training different neural network models and tuning them for optimal performance Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more A hands-on guide covering the common as well as the not so common problems in deep learning using Python Who This Book Is For This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired. What You Will Learn Implement different neural network models in Python Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras Apply tips and tricks related to neural networks internals, to boost learning performances Consolidate machine learning principles and apply them in the deep learning field Reuse and adapt Python code snippets to everyday problems Evaluate the cost/benefits and performance implication of each discussed solution In Detail Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios. Style and approach Unique blend of independent recipes arranged in the most logical manner

Tensorflow 2 0 Computer Vision Cookbook

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Author : Jesus Martinez
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-26

Tensorflow 2 0 Computer Vision Cookbook written by Jesus Martinez and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Computers categories.

Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques Key FeaturesDevelop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2.xDiscover practical recipes to overcome various challenges faced while building computer vision modelsEnable machines to gain a human level understanding to recognize and analyze digital images and videosBook Description Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow. The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's key features, such as the Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO). Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks. By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x. What you will learnUnderstand how to detect objects using state-of-the-art models such as YOLOv3Use AutoML to predict gender and age from imagesSegment images using different approaches such as FCNs and generative modelsLearn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentationEnable machines to recognize people's emotions in videos and real-time streamsAccess and reuse advanced TensorFlow Hub models to perform image classification and object detectionGenerate captions for images using CNNs and RNNsWho this book is for This book is for computer vision developers and engineers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.