Python Image Processing Cookbook

Python Image Processing Cookbook Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Python Image Processing Cookbook book. This book definitely worth reading, it is an incredibly well-written.

Python Image Processing Cookbook

Sandipan Dey
Python Image Processing Cookbook Book PDF

Author : Sandipan Dey
Publisher : Packt Publishing Ltd
Page : 429 pages
File Size : 51,8 Mb
Release : 2020-04-17
Category : Computers
ISBN : 9781789535181

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Python Image Processing Cookbook by Sandipan Dey Pdf

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.

Hands-On Image Processing with Python

Sandipan Dey
Hands On Image Processing with Python Book PDF

Author : Sandipan Dey
Publisher : Packt Publishing Ltd
Page : 483 pages
File Size : 49,7 Mb
Release : 2018-11-30
Category : Computers
ISBN : 9781789341850

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Hands-On Image Processing with Python by Sandipan Dey Pdf

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.

OpenCV 3 Computer Vision with Python Cookbook

Aleksei Spizhevoi,Aleksandr Rybnikov
OpenCV 3 Computer Vision with Python Cookbook Book PDF

Author : Aleksei Spizhevoi,Aleksandr Rybnikov
Publisher : Packt Publishing Ltd
Page : 296 pages
File Size : 55,5 Mb
Release : 2018-03-23
Category : Computers
ISBN : 9781788478755

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OpenCV 3 Computer Vision with Python Cookbook by Aleksei Spizhevoi,Aleksandr Rybnikov Pdf

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 ...

Python 3 Image Processing

Pajankar Ashwin
Python 3 Image Processing Book PDF

Author : Pajankar Ashwin
Publisher : BPB Publications
Page : 252 pages
File Size : 42,6 Mb
Release : 2019-09-20
Category : Computers
ISBN : 9789389328110

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Python 3 Image Processing by Pajankar Ashwin Pdf

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/

Machine Learning with Python Cookbook

Chris Albon
Machine Learning with Python Cookbook Book PDF

Author : Chris Albon
Publisher : "O'Reilly Media, Inc."
Page : 305 pages
File Size : 47,8 Mb
Release : 2018-03-09
Category : Computers
ISBN : 9781491989333

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Machine Learning with Python Cookbook by Chris Albon Pdf

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays 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 Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

Image Processing and Acquisition using Python

Ravishankar Chityala,Sridevi Pudipeddi
Image Processing and Acquisition using Python Book PDF

Author : Ravishankar Chityala,Sridevi Pudipeddi
Publisher : CRC Press
Page : 335 pages
File Size : 47,6 Mb
Release : 2020-06-11
Category : Mathematics
ISBN : 9780429516528

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Image Processing and Acquisition using Python by Ravishankar Chityala,Sridevi Pudipeddi Pdf

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.

Image Processing Masterclass with Python

Sandipan Dey
Image Processing Masterclass with Python Book PDF

Author : Sandipan Dey
Publisher : BPB Publications
Page : 428 pages
File Size : 52,7 Mb
Release : 2021-03-10
Category : Computers
ISBN : 9789389898644

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Image Processing Masterclass with Python by Sandipan Dey Pdf

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

Python Natural Language Processing Cookbook

Zhenya Antić
Python Natural Language Processing Cookbook Book PDF

Author : Zhenya Antić
Publisher : Packt Publishing Ltd
Page : 285 pages
File Size : 42,6 Mb
Release : 2021-03-19
Category : Computers
ISBN : 9781838987787

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Python Natural Language Processing Cookbook by Zhenya Antić Pdf

Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualization Key FeaturesAnalyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensimImplement common and not-so-common linguistic processing tasks using Python librariesOvercome the common challenges faced while implementing NLP pipelinesBook Description Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing. What you will learnBecome well-versed with basic and advanced NLP techniques in PythonRepresent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddingsPerform text classification using different methods, including SVMs and LSTMsExplore different techniques for topic modeling such as K-means, LDA, NMF, and BERTWork with visualization techniques such as NER and word clouds for different NLP toolsBuild a basic chatbot using NLTK and RasaExtract information from text using regular expression techniques and statistical and deep learning toolsWho this book is for This book is for data scientists and professionals who want to learn how to work with text. Intermediate knowledge of Python will help you to make the most out of this book. If you are an NLP practitioner, this book will serve as a code reference when working on your projects.

Python Cookbook

Alex Martelli,Anna Ravenscroft,David Ascher
Python Cookbook Book PDF

Author : Alex Martelli,Anna Ravenscroft,David Ascher
Publisher : "O'Reilly Media, Inc."
Page : 847 pages
File Size : 46,6 Mb
Release : 2005-03-18
Category : Computers
ISBN : 9780596554743

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Python Cookbook by Alex Martelli,Anna Ravenscroft,David Ascher Pdf

Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. It is now being used by an increasing number of major organizations, including NASA and Google.Updated for Python 2.4, The Python Cookbook, 2nd Edition offers a wealth of useful code for all Python programmers, not just advanced practitioners. Like its predecessor, the new edition provides solutions to problems that Python programmers face everyday.It now includes over 200 recipes that range from simple tasks, such as working with dictionaries and list comprehensions, to complex tasks, such as monitoring a network and building a templating system. This revised version also includes new chapters on topics such as time, money, and metaprogramming.Here's a list of additional topics covered: Manipulating text Searching and sorting Working with files and the filesystem Object-oriented programming Dealing with threads and processes System administration Interacting with databases Creating user interfaces Network and web programming Processing XML Distributed programming Debugging and testing Another advantage of The Python Cookbook, 2nd Edition is its trio of authors--three well-known Python programming experts, who are highly visible on email lists and in newsgroups, and speak often at Python conferences.With scores of practical examples and pertinent background information, The Python Cookbook, 2nd Edition is the one source you need if you're looking to build efficient, flexible, scalable, and well-integrated systems.

Deep Learning Cookbook

Douwe Osinga
Deep Learning Cookbook Book PDF

Author : Douwe Osinga
Publisher : "O'Reilly Media, Inc."
Page : 252 pages
File Size : 49,5 Mb
Release : 2018-06-05
Category : Computers
ISBN : 9781491995792

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Deep Learning Cookbook by Douwe Osinga Pdf

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections

PyTorch 1.x Reinforcement Learning Cookbook

Yuxi (Hayden) Liu
PyTorch 1 x Reinforcement Learning Cookbook Book PDF

Author : Yuxi (Hayden) Liu
Publisher : Packt Publishing Ltd
Page : 334 pages
File Size : 54,7 Mb
Release : 2019-10-31
Category : Computers
ISBN : 9781838553234

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PyTorch 1.x Reinforcement Learning Cookbook by Yuxi (Hayden) Liu Pdf

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.

IPython Interactive Computing and Visualization Cookbook

Cyrille Rossant
IPython Interactive Computing and Visualization Cookbook Book PDF

Author : Cyrille Rossant
Publisher : Packt Publishing Ltd
Page : 512 pages
File Size : 50,7 Mb
Release : 2014-09-25
Category : Computers
ISBN : 9781783284825

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IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant Pdf

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

Python Data Analysis Cookbook

Ivan Idris
Python Data Analysis Cookbook Book PDF

Author : Ivan Idris
Publisher : Packt Publishing Ltd
Page : 462 pages
File Size : 42,9 Mb
Release : 2016-07-22
Category : Computers
ISBN : 9781785283857

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Python Data Analysis Cookbook by Ivan Idris Pdf

Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.

TensorFlow 2.0 Computer Vision Cookbook

Jesus Martinez
TensorFlow 2 0 Computer Vision Cookbook Book PDF

Author : Jesus Martinez
Publisher : Packt Publishing Ltd
Page : 542 pages
File Size : 45,7 Mb
Release : 2021-02-26
Category : Computers
ISBN : 9781838820688

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TensorFlow 2.0 Computer Vision Cookbook by Jesus Martinez Pdf

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.

Python Feature Engineering Cookbook

Soledad Galli
Python Feature Engineering Cookbook Book PDF

Author : Soledad Galli
Publisher : Packt Publishing Ltd
Page : 364 pages
File Size : 47,8 Mb
Release : 2020-01-22
Category : Computers
ISBN : 9781789807820

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Python Feature Engineering Cookbook by Soledad Galli Pdf

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems. What you will learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.