Download or read book Image Processing Masterclass with Python written by Sandipan Dey and published by BPB Publications. This book was released on 2021-03-10 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 50 problems solved with classical algorithms + ML / DL models KEY FEATURESÊ _ Problem-driven approach to practice image processing.Ê _ Practical usage of popular Python libraries: Numpy, Scipy, scikit-image, PIL and SimpleITK. _ End-to-end demonstration of popular facial image processing challenges using MTCNN and MicrosoftÕs Cognitive Vision APIs. Ê DESCRIPTIONÊ 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.Ê 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 differentÊ 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 LEARNÊÊ _ Develop strong grip on the fundamentals of Image Processing and Image Manipulation. _ Solve popular Image Processing problems using Machine Learning and Deep Learning models. _ Working knowledge on Python libraries including numpy, scipyÊ and scikit-image. _ Use popular Python Machine Learning packages such as scikit-learn, Keras and pytorch. _ Live implementation of Facial Image Processing techniques such as Face Detection / Recognition / Parsing dlib and MTCNN. WHO THIS BOOK IS FORÊÊÊ 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: Facel Image Processing

Download or read book Hands On Image Processing with Python written by Sandipan Dey and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks. Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook 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 learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho 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.

Download or read book Python Image Processing Cookbook written by Sandipan Dey and published by Packt Publishing Ltd. This book was released on 2020-04-17 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Elements of Deep Learning for Computer Vision written by Bharat Sikka and published by BPB Publications. This book was released on 2021-06-24 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World

Download or read book Python 3 Image Processing written by Pajankar Ashwin and published by BPB Publications. This book was released on 2019-09-20 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a working knowledge of practical image processing and with scikit-image.Key features Comprehensive coverage of various aspects of scientific Python and concepts in image processing. Covers various additional topics such as Raspberry Pi, conda package manager, and Anaconda distribution of Python. Simple language, crystal clear approach, and straight forward comprehensible presentation of concepts followed by code examples and output screenshots. Adopting user-friendly style for explanation of code examples.DescriptionThe book has been written in such a way that the concepts are explained in detail, giving adequate emphasis on code examples. To make the topics more comprehensive, screenshots and code samples are furnished extensively throughout the book. The book is conceptualized and written in such a way that the beginner readers will find it very easy to understand the concepts and implement the programs.The book also features the most current version of Raspberry Pi and associated software with it. This book teaches novice beginners how to write interesting image processing programs with scientific Python ecosystem. The book will also be helpful to experienced professionals to make transition to rewarding careers in scientific Python and computer vision. What will you learn Raspberry Pi, Python 3 Basics Scientific Python Ecosystem NumPy and Matplotlib Visualization with Matplotlib Basic NumPy, Advanced Image Processing with NumPy and Matplotlib Getting started with scikit-image Thresholding, Histogram Equalization, and Transformations Kernels, Convolution, and Filters Morphological Operations and Image Restoration Noise Removal and Edge Detection Advanced Image Processing OperationsWho this book is for Students pursuing BE/BSc/ME/MSc/BTech/MTech in Computer Science, Electronics, Electrical, and Mathematics Python enthusiasts Computer Vision and Image Processing professionals Anyone fond of tinkering with Raspberry Pi Researchers in Computer Vision Table of contents1. Concepts in Image Processing2. Installing Python 3 on Windows3. Introduction to Raspberry Pi4. Python 3 Basics5. Introduction to the Scientific Python Ecosystem6. Introduction to NumPy and Matplotlib7. Visualization with Matplotlib8. Basic Image Processing with NumPy and Matplotlib9. Advanced Image Processing with NumPy and Matplotlib10. Getting Started with Scikit-Image11. Thresholding Histogram Equalization and Transformations12. Kernels, Convolution and Filters13. Morphological Operations and Image Restoration14. Noise Removal and Edge Detection15. Advanced Image Processing Operations16. Wrapping UpAbout the authorAshwin Pajankar is a polymath. He has more than two decades of programming experience. He is a Science Popularizer, a Programmer, a Maker, an Author, and a Youtuber. He is passionate about STEM (Science-Technology-Education-Mathematics) education. He is also a freelance software developer and technology trainer. He graduated from IIIT Hyderabad with M.Tech. in Computer Science and Engineering. He has worked in a few multinational corporations including Cisco Systems and Cognizant for more than a decade. Ashwin is also an online trainer with various eLearning platforms like BPBOnline, Udemy, and Skillshare. In his free time, he consults on the topics of Python programming and data science to the local software companies in the city of Nasik. He is actively involved in various social initiatives and has won many accolades during his student life and at his past workplaces.His Website: http://www.ashwinpajankar.com/His LinkedIn Profile: https://www.linkedin.com/in/ashwinpajankar/

Download or read book Fundamentals of Deep Learning and Computer Vision written by Nikhil Singh and published by BPB Publications. This book was released on 2020-02-24 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Computer Vision concepts using Deep Learning with easy-to-follow steps DESCRIPTIONÊ This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons. To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model.Ê Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification. KEY FEATURESÊ Setting up the Python and TensorFlow environment Learn core Tensorflow concepts with the latest TF version 2.0 Learn Deep Learning for computer vision applicationsÊ Understand different computer vision concepts and use-cases Understand different state-of-the-art CNN architecturesÊ Build deep neural networks with transfer Learning using features from pre-trained CNN models Apply computer vision concepts with easy-to-follow code in Jupyter Notebook WHAT WILL YOU LEARNÊ This book will help the readers to understand and apply the latest Deep Learning technologies to different interesting computer vision applications without any prior domain knowledge of image processing. Thus, helping the users to acquire new skills specific to Computer Vision and Deep Learning and build solutions to real-life problems such as Image Classification and Object Detection. This book will serve as a basic guide for all the beginners to master Deep Learning and Computer Vision with lucid and intuitive explanations using basic mathematical concepts. It also explores these concepts with popular the deep learning framework TensorFlow. WHO THIS BOOK IS FOR This book is for all the Data Science enthusiasts and practitioners who intend to learn and master Computer Vision concepts and their applications using Deep Learning. This book assumes a basic Python understanding with hands-on experience. A basic senior secondary level understanding of Mathematics will help the reader to make the best out of this book.Ê Table of Contents 1. Introduction to TensorFlow 2. Introduction to Neural NetworksÊ 3. Convolutional Neural NetworkÊÊ 4. CNN Architectures 5. Sequential Models

Download or read book A Python Guide for Web Scraping written by Pradumna Milind Panditrao and published by BPB Publications. This book was released on 2021-05-18 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on training on any web crawling/scraping tool and uses of web scraping in the real-time industry Ê KEY FEATURESÊÊ _ Includes numerous use-cases on the use of web scraping for industrial applications. _ Learn how to automate web scraping tasks. _ Explore ready-made syntaxes of Python scripts to run web scraping. DESCRIPTIONÊ A Python Guide for Web Scraping is a book that will give information about the importance of web scraping using Python. It includes real-time examples of web scraping. It implies the automation use cases of web scraping as well. It gives information about the different tools and libraries of web scraping so that readers get a wide idea about the features and existence of web scraping. In this book, we started with the basics of Python and its syntactical information. We briefed about the use cases and features of Python. We have explained the importance of Python in automation systems. Furthermore, we have added information about real-time industrial examples. We have concentrated and deep-dived into PythonÕs importance in web scraping, explained the different tools and their usages. We have explained the real-time industrial domain-wise use cases for web scraping. WHAT YOU WILL LEARN _ Explore the Python syntax and key features of using Python for web scraping. _ Usage of Python in the web scraping tasks and how to automate scraping. _ How to use different libraries and modules of Python. WHO THIS BOOK IS FORÊÊ This book is basically for data engineers and data programmers who have a basic knowledge of Python and for theÊ readers who want to learn about web scraping projects for industries. TABLE OF CONTENTS 1. Python Basics 2. Use Cases of Python 3. Automation Using Python 4. Industrial Automation-Python 5. Web Scraping 6. Web Scraping and Necessity 7. Python - Web Scraping and Different Tools 8. Automation in Web Scraping 9. Use Cases-Web Scraping 10. Industrial Benefits of Web Scraping

Download or read book Hands On Data Science and Python Machine Learning written by Frank Kane and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Download or read book An Interdisciplinary Introduction to Image Processing written by Steven Tanimoto and published by MIT Press (MA). This book was released on 2012 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores image processing from several perspectives: the creative, the theoretical (mainly mathematical), and the programmatical. It explains the basic principles of image processing, drawing on key concepts and techniques from mathematics, psychology of perception, computer science, and art, and introduces computer programming as a way to get more control over image processing operations. It does so without requiring college-level mathematics or prior programming experience. The content is supported by PixelMath, a freely available software program that helps the reader understand images as both visual and mathematical objects. The first part of the book covers such topics as digital image representation, sampling, brightness and contrast, color models, geometric transformations, synthesizing images, stereograms, photomosaics, and fractals. The second part of the book introduces computer programming using an open-source version of the easy-to-learn Python language. It covers the basics of image analysis and pattern recognition, including edge detection, convolution, thresholding, contour representation, and K-nearest-neighbor classification. A chapter on computational photography explores such subjects as high-dynamic-range imaging, autofocusing, and methods for automatically inpainting to fill gaps or remove unwanted objects in a scene. Applications described include the design and implementation of an image-based game. The PixelMath software provides a "transparent" view of digital images by allowing the user to view the RGB values of pixels by zooming in on an image. PixelMath provides three interfaces: the pixel calculator; the formula page, an advanced extension of the calculator; and the Python window.

Download or read book Image Processing and Acquisition using Python written by Ravishankar Chityala and published by CRC Press. This book was released on 2020-06-11 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples. A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The second part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples, detailed derivations, and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test the reader’s skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book’s web page. New to this edition Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules.

Download or read book Pillow written by Michael Driscoll and published by . This book was released on 2021-03-18 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pillow: Image Processing with Python is the only book that covers the Pillow package, the friendly fork of the Python Imaging Library (PIL). The first few chapters of the book will get you started down the path of knowledge and help you understand how to use Pillow effectively. This book is printed in FULL COLOR. In Pillow: Image Processing with Python, you will learn how to: Crop photos Apply filters Work with colors Combine photos Extract metadata Drawing text and shapes on image Create simple image GUIs You'll learn all these things and more in this book. Soon you will be able to edit photos like a professional using the Python programming language!

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Download or read book Image Processing and Acquisition using Python written by Ravishankar Chityala and published by CRC Press. This book was released on 2020-06-11 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples. A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The second part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples, detailed derivations, and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test the reader’s skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book’s web page. New to this edition Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules.

Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Download or read book Digital Image Processing 2 e written by Gonzalez and published by Pearson Education India. This book was released on 2002 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hands on MuleSoft Anypoint platform Volume 1 written by Nanda Nachimuthu and published by BPB Publications. This book was released on 2021-01-02 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn, develop, test and document powerful yet simple RAML API specifications using MuleSoft API Designer and API Toolkit. KEY FEATURES ¥ Explore concept of API and its significance in enterprise applications ¥ Design your own API using Mulesoft Anypoint Platform ¥ Exciting coverage on how API works in Enterprise Applications ¥ Live demonstration on how to build and integrate API with end-to-end implementation and working codes DESCRIPTION Hands-on MuleSoft Anypoint platform book directs you step-by-step in designing API, its Implementation, and how to integrate smartly with other applications. This book is enriched with lots of interactive screenshots and working source codes. Throughout this book, you will learn key industry insights on System Integration, API Led Connectivity, Centre for Enablement, and RAML. This book will talk about how to use publicly available free mock REST APIs and how to call and test them from RESTful clients like Postman. You can also see some of the commercially available license-based APIs. Equipped with exercises, you will practice developing your own RESTful API specification along with how to add, retrieve, update, and delete data for your business use. You will be using the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. At the end of the book, you will be summarizing your learnings with an end-to-end implementation demonstration on the API design and its implementation. WHAT YOU WILL LEARN ¥ Know-how of public APIs, commercial APIs, and cloud-based SaaS APIs ¥ Role of Mulesoft in SaaS applications ¥ You learn to design and test the API development and implementation ¥ You get handy with all the features and mechanism of Mulesoft Anypoint Platform ¥ Get a clear vision of what is Enterprise Integration and what are all the opportunities coming up in near future. WHO THIS BOOK IS FOR This book is for fresher, IT employees with less or no programming background such as Business Analysts, Quality Engineers, HR, Technical persons who are looking for a change in technology area if they are working in outdated technologies. TABLE OF CONTENTS 1. MuleSoft Fundamentals 2. MuleSoft Internals 3. MuleSoft Salient features 4. From ESB to API Led Connectivity 5. Cloud based SaaS Applications and MuleSoft Connectors 6. REST, SOAP, Postman and Anypoint Studio 7. Start RAML 8. RAML in detail 9. RAML Project

Download or read book Mastering OpenCV with Python written by Ayush Vaishya and published by Orange Education Pvt Ltd. This book was released on 2023-11-15 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlocking Visual Insights: OpenCV Made Simple and Powerful. KEY FEATURES ● OpenCV Mastery: Harness the full potential of OpenCV. ● Comprehensive Coverage: From fundamentals to advanced techniques. ● Practical Exercises: Apply knowledge through hands-on tasks. DESCRIPTION "Mastering OpenCV with Python" immerses you in the captivating realm of computer vision, with a structured approach that equips you with the knowledge and skills essential for success in this rapidly evolving field. From grasping the fundamental concepts of image processing and OpenCV to mastering advanced techniques such as neural networks and object detection, you will gain a comprehensive understanding. Each chapter is enriched with hands-on exercises and real-world projects, ensuring the acquisition of practical skills that can be immediately applied in your professional journey. This book not only elevates your technical proficiency but also prepares you for a rewarding career. The technological job landscape is constantly evolving, and professionals who can harness the potential of computer vision are in high demand. By mastering the skills and insights contained within these pages, you will be well-prepared to explore exciting career opportunities, ranging from machine learning engineering to computer vision research. This book is your ticket to a future filled with innovation and professional advancement within the dynamic world of computer vision. WHAT WILL YOU LEARN ● Master Image Processing and Machine Learning with OpenCV using advanced Tools and Libraries. ● Create Real-World Projects with Hands-On Experience. ● Explore Machine Learning for Computer Vision. ● Develop Confidence in Practical Computer Vision Projects. ● Conquer Real-World Image Processing Challenges. ● Apply Computer Vision Across Diverse Industries. ● Boost Your Career in Computer Vision. ● Become an Expert in Computer Vision for Career Advancement. WHO IS THIS BOOK FOR? This beginner-friendly book in computer vision requires no prior experience, making it accessible to newcomers. While a basic programming understanding is helpful, it's designed to guide individuals from diverse backgrounds into the captivating realms of AI, computer vision, and image processing. It's equally valuable for aspiring tech professionals, students, and enthusiasts seeking rewarding careers and knowledge in these cutting-edge fields. TABLE OF CONTENTS 1. Introduction to Computer Vision 2. Getting Started with Images 3. Image Processing Fundamentals 4. Image Operations 5. Image Histograms 6. Image Segmentation 7. Edges and Contours 8. Machine Learning with Images 9. Advanced Computer Vision Algorithms 10. Neural Networks 11. Object Detection Using OpenCV 12. Projects Using OpenCV Index