Buku Pintar Visual Basic Untuk Pelajar Dan Mahasiswa
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Buku Pintar Visual Basic Untuk Pelajar Dan Mahasiswa
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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-15
Buku Pintar Visual Basic Untuk Pelajar Dan Mahasiswa written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-15 with Computers categories.
Telah banyak buku pemrograman Visual Basic .NET dipublikasikan dan didistribusikan. Faktanya, sangat sedikit yang mengupas dasar pengenalan Visual Basic .NET secara komprehensif dan yang merangkum topik bahasan secara detil dan efektif. Sementara itu, banyak para mahasiswa, insinyur, peneliti, maupun pengembang perangkat lunak yang tidak berkesempatan belajar Visual Basic .NET di universitas, tetapi tetap berkeinginan untuk menguasai Visual Basic .NET dengan berlatih setiap hari. Oleh karena itu, buku ini, yang berorientasi-contoh langkah-demi-langkah, memberikan kesempatan kepada setiap pembaca untuk belajar Visual Basic mulai dari nol sampai benar-benar menguasai. Buku ini mengungkap secara komprehensif: komponen-komponen utama Visual Basic .NET yang meliputi tipe data dan variabel; struktur seleksi dan repetisi, prosedur, fungsi, array, dan file dan struktur. Karena sifatnya yang dasar dan komprehensif, buku ini cocok untuk programer pemula, baik untuk mahasiswa maupun siswa SMU/SMK. Anda mungkin tidak langsung menjadi pakar Visual Basic .NET setelah membaca buku ini, tetapi Anda telah bersiap-siap menjadi salah satu orang yang mahir memprogram Visual Basic .NET, karena buku ini didesain untuk membantu Anda menjadi programmer Visual Basic .NET yang tangguh.
Buku Pintar Pemrograman C Untuk Pelajar Dan Mahasiswa
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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-15
Buku Pintar Pemrograman C Untuk Pelajar Dan Mahasiswa written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-15 with Computers categories.
Puji syukur kepada Tuhan Yang Maha Kuasa atas tuntasnya penulisan buku ini. Buku ini dikonsentrasikan pada penjelasan sederhana atas tiap teknik yang menjadi bahasan. Buku ini ini untuk setiap orang yang ingin belajar bagaimana memprogram C# menggunakan .NET Framework. Beberapa bab awal pada buku ini ditujukan bagi mereka yang belum memiliki pengalaman dalam memprogram. Jika Anda telah memiliki keterampilan pemrograman bahasa pemrograman lain, maka banyak materi pada buku ini akan familiar bagi Anda. Banyak aspek pada sintaks C# memiliki kesamaan dengan bahasa permrograman lain (khususnya dengan Java). Jadi, jika Anda masih belum familiar dengan .NET Framework tetapi telah berpengalaman dalam memprogram, Anda sebaiknya memulai dari Bab 1 dan kemudian membaca cepat beberapa bab sesudahnya sebelum memasuki bab-bab yang berkaitan dengan pemrograman berorientasi objek pada C#. Buku ini ditujukan bagi pemula karena difokuskan pada aspek-aspek mendasar dari pemrograman C#. Setelah membaca buku ini, Anda akan memahami bagaimana mendefinisikan metode, properti, indekser, kelas dan antarmuka pada C#. Penjelasan tiap program diberikan baris demi baris, sehingga detil informasi di balik setiap kode dapat dipahami dengan lengkap.
Buku Pintar Visual Basic Dan Sql Server
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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-15
Buku Pintar Visual Basic Dan Sql Server written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-15 with Computers categories.
Telah banyak buku pemrograman Visual Basic .NET dipublikasikan dan didistribusikan. Faktanya, sangat sedikit yang mengupas dasar pengenalan Visual Basic .NET secara komprehensif dan yang merangkum topik bahasan secara detil dan efektif. Sementara itu, banyak para mahasiswa, insinyur, peneliti, maupun pengembang perangkat lunak yang tidak berkesempatan belajar Visual Basic .NET di universitas, tetapi tetap berkeinginan untuk menguasai Visual Basic .NET dengan berlatih setiap hari. Buku ini mengungkap secara komprehensif: kelas, kontrol dan antarmuka, koleksi, dan database SQL SERVER, dan aplikasi database. Setiap bab berorientasi-contoh langkah-demi-langkah, memberikan kesempatan kepada setiap pembaca untuk belajar Visual Basic mulai dari nol sampai benar-benar menguasai. Berikut adalah bab-bab yang dibahas secara detil pada buku ini: Bab 1 Kelas; Bab 2 Validasi Masukan dan Antarmuka User; Bab 3 Koleksi; Bab 4 Menggunakan Database SQL Server; dan Bab 5 Aplikasi Database. Anda mungkin tidak langsung menjadi pakar Visual Basic .NET setelah membaca buku ini, tetapi Anda telah bersiap-siap menjadi salah satu orang yang mahir memprogram Visual Basic .NET, karena buku ini didesain untuk membantu Anda menjadi programmer Visual Basic .NET yang tangguh.
Buku Latihan Visual Basic Untuk Mahasiswa
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Author : Jubilee Enterprise
language : id
Publisher: Elex Media Komputindo
Release Date : 2015-05-05
Buku Latihan Visual Basic Untuk Mahasiswa written by Jubilee Enterprise and has been published by Elex Media Komputindo this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-05 with Computers categories.
Visual Basic memiliki daya tarik jika dilihat dari sisi kemudahan dalam pemrogramannya. Anda dapat membuat aplikasi hitung-hitungan sederhana sampai aplikasi yang melibatkan banyak form dan konektivitas database. Tool yang digunakan adalah Visual Studio Express 2013 (gratis dan bisa diunduh langsung dari website Microsoft). Ditujukan untuk semua pembaca, terutama mahasiswa yang ingin mencari pengetahuan tentang pemrograman Visual Basic. Dengan mempelajari Visual Basic, diharapkan para mahasiswa dapat mengerjakan tugas akhir pembuatan aplikasi yang melibatkan bahasa pemrograman tersebut. Materi mellputl pengenalan Visual Basic, database, dan pembuatan aplikasi-aplikasi sederhana..
Buku Pintar Vb Net
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Author : Herry Raditya Wibowo,Jubilee
language : id
Publisher: Elex Media Komputindo
Release Date : 2014-05-19
Buku Pintar Vb Net written by Herry Raditya Wibowo,Jubilee and has been published by Elex Media Komputindo this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
"""Buku ini membahasa pemrograman VB.Net untuk level pemula. Pembahasannya di mulai dari dasar-dasar pemrograman VB.Net sampai pada masalah database dan mengatasi kesalahan pada aplikasi.Pembahasan dalam buku ini menggunakan Visual Studio 2013 Express yang bisa anda download dan gunakan secara gratis sampai kapanpun. Pembahasan selengkapnya meliputi : * Pengenalan VB.Net * Pembuatan aplikasi sederhana * Pembuatan variable,loop,kondisi,dan sebagainya * Pembuatan interface aplikasi * Database dengan VB.Net * Debugging dan penanganan kesalahan"""
Bahasa Pemrograman Populer
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Author : Tutuk Indriyani
language : id
Publisher: PT. Sonpedia Publishing Indonesia
Release Date : 2024-01-25
Bahasa Pemrograman Populer written by Tutuk Indriyani and has been published by PT. Sonpedia Publishing Indonesia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-25 with Computers categories.
Buku "Bahasa Pemrograman Populer" mengungkap esensi dari bahasa pemrograman utama dengan merinci poin-poin kunci. Buku ini memulai perjalanannya dengan pengantar yang menyajikan landasan dasar dan pentingnya pemahaman berbagai bahasa pemrograman dalam dunia teknologi saat ini. Dari sana, pembaca dihadapkan pada keunggulan Python sebagai bahasa serba guna yang mendominasi ilmu data dan pengembangan perangkat lunak. Dilanjutkan dengan eksplorasi peran penting JavaScript dalam pengembangan web, Java sebagai bahasa cross-platform, dan C# dengan ekosistem .NET untuk aplikasi Windows dan web. Poin akhir mengenai PHP menyoroti perannya dalam pengembangan web dinamis. Buku ini menggabungkan penjelasan yang jelas dan aplikasi praktis, memberikan pembaca pemahaman yang mendalam tentang bahasa-bahasa kunci yang membentuk lanskap pemrograman modern. Dengan fokus pada Python, JavaScript, Java, C#, dan PHP, buku ini menjadi panduan yang tak ternilai bagi mereka yang ingin menjelajahi dan menguasai dunia bahasa pemrograman.
Learn From Scratch Machine Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-03-03
Learn From Scratch Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-03 with Computers categories.
In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: 6.1 Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt.
The Practical Guides On Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-17
The Practical Guides On Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-17 with Computers categories.
In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display image histogram. It is a graphical representation that displays the distribution of pixel intensities in an image. It provides information about the frequency of occurrence of each intensity level in the image. The histogram allows us to understand the overall brightness or contrast of the image and can reveal important characteristics such as dynamic range, exposure, and the presence of certain image features. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. The MNIST dataset is a widely used dataset in machine learning and computer vision, particularly for image classification tasks. It consists of a collection of handwritten digits from zero to nine, where each digit is represented as a 28x28 grayscale image. The dataset was created by collecting handwriting samples from various individuals and then preprocessing them to standardize the format. Each image in the dataset represents a single digit and is labeled with the corresponding digit it represents. The labels range from 0 to 9, indicating the true value of the handwritten digit. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset. Following are the steps taken in this chapter: Dataset Exploration: Explore the Brain Image MRI dataset from Kaggle. Describe the structure of the dataset, the different classes (tumor vs. non-tumor), and any preprocessing steps required; Data Preprocessing: Preprocess the dataset to prepare it for model training. This may include tasks such as resizing images, normalizing pixel values, splitting data into training and testing sets, and creating labels; Model Building: Use TensorFlow and Keras to build a deep learning model for brain tumor detection. Choose an appropriate architecture, such as a convolutional neural network (CNN), and configure the model layers; Model Training: Train the brain tumor detection model using the preprocessed dataset. Specify the loss function, optimizer, and evaluation metrics. Monitor the training process and visualize the training/validation accuracy and loss over epochs; Model Evaluation: Evaluate the trained model on the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Prediction and Visualization: Use the trained model to make predictions on new MRI images. Visualize the predicted results alongside the ground truth labels to demonstrate the effectiveness of the model. Finally, you will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle using MobileNetV2 and CNN models. Following are the steps taken in this chapter: Data Exploration: Load the dataset using Pandas, perform exploratory data analysis (EDA) to gain insights into the data, and visualize the distribution of gender classes; Data Preprocessing: Preprocess the dataset by performing necessary transformations, such as resizing images, converting labels to numerical format, and splitting the data into training, validation, and test sets; Model Building: Use TensorFlow and Keras to build a gender classification model. Define the architecture of the model, compile it with appropriate loss and optimization functions, and summarize the model's structure; Model Training: Train the model on the training set, monitor its performance on the validation set, and tune hyperparameters if necessary. Visualize the training history to analyze the model's learning progress; Model Evaluation: Evaluate the trained model's performance on the test set using various metrics such as accuracy, precision, recall, and F1 score. Generate a classification report and a confusion matrix to assess the model's performance in detail; Prediction and Visualization: Use the trained model to make gender predictions on new, unseen data. Visualize a few sample predictions along with the corresponding images. Finally, you will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset using CNN model. The FER2013 dataset contains facial images categorized into seven different emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. To perform facial expression recognition using this dataset, you would typically follow these steps; Data Preprocessing: Load and preprocess the dataset. This may involve resizing the images, converting them to grayscale, and normalizing the pixel values; Data Split: Split the dataset into training, validation, and testing sets. The training set is used to train the model, the validation set is used to tune hyperparameters and evaluate the model's performance during training, and the testing set is used to assess the final model's accuracy; Model Building: Build a deep learning model using TensorFlow and Keras. This typically involves defining the architecture of the model, selecting appropriate layers (such as convolutional layers, pooling layers, and fully connected layers), and specifying the activation functions and loss functions; Model Training: Train the model using the training set. This involves feeding the training images through the model, calculating the loss, and updating the model's parameters using optimization techniques like backpropagation and gradient descent; Model Evaluation: Evaluate the trained model's performance using the validation set. This can include calculating metrics such as accuracy, precision, recall, and F1 score to assess how well the model is performing; Model Testing: Assess the model's accuracy and performance on the testing set, which contains unseen data. This step helps determine how well the model generalizes to new, unseen facial expressions; Prediction: Use the trained model to make predictions on new images or live video streams. This involves detecting faces in the images using OpenCV, extracting facial features, and feeding the processed images into the model for prediction. Then, you will also build a GUI application for this purpose.
Learn From Scratch Signal And Image Processing With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-14
Learn From Scratch Signal And Image Processing With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Technology & Engineering categories.
In this book, you will learn how to use OpenCV, NumPy library and other libraries to perform signal processing, image processing, object detection, and feature extraction with Python GUI (PyQt). You will learn how to filter signals, detect edges and segments, and denoise images with PyQt. You will also learn how to detect objects (face, eye, and mouth) using Haar Cascades and how to detect features on images using Harris Corner Detection, Shi-Tomasi Corner Detector, Scale-Invariant Feature Transform (SIFT), and Features from Accelerated Segment Test (FAST). In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. In Chapter 4, you will learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, and Tutorial Steps To Implement Image Denoising. In Chapter 5, you will learn: Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, and Tutorial Steps To Extract Detected Objects. In Chapter 6, you will learn: Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). You can download the XML files from https://viviansiahaan.blogspot.com/2023/06/learn-from-scratch-signal-and-image.html.
Head First Programming
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Author : David Griffiths
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2009-11-16
Head First Programming written by David Griffiths 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 2009-11-16 with Computers categories.
Looking for a reliable way to learn how to program on your own, without being overwhelmed by confusing concepts? Head First Programming introduces the core concepts of writing computer programs -- variables, decisions, loops, functions, and objects -- which apply regardless of the programming language. This book offers concrete examples and exercises in the dynamic and versatile Python language to demonstrate and reinforce these concepts. Learn the basic tools to start writing the programs that interest you, and get a better understanding of what software can (and cannot) do. When you're finished, you'll have the necessary foundation to learn any programming language or tackle any software project you choose. With a focus on programming concepts, this book teaches you how to: Understand the core features of all programming languages, including: variables, statements, decisions, loops, expressions, and operators Reuse code with functions Use library code to save time and effort Select the best data structure to manage complex data Write programs that talk to the Web Share your data with other programs Write programs that test themselves and help you avoid embarrassing coding errors We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Programming uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.