Buku Pintar Pemrograman Java Untuk Pelajar Dan Mahasiswa

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Buku Pintar Pemrograman Java Untuk Pelajar Dan Mahasiswa

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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-15

Buku Pintar Pemrograman Java 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 ditulis karena spirit untuk mendokumentasikan gagasan-gagasan pemrograman berorientasi objek di dalam keluarga besar JAVA. Di Indonesia, sangat jarang ditemui buku yang mendiskusikan pemrograman JAVA yang mengupas secara detil kelebihan dan kekurangan suatu kode sumber. Buku ini menelaah suatu kode sumber dengan memberikan perhatian khusus terhadap potongan-potongan kode yang dianggap penting. Buku ini dikhususkan bagi mahasiswa sarjana dan pembelajar mandiri yang menjadi pemrogram aktif. Penulis mengucapkan penghargaan yang tinggi kepada semua pihak yang telah memberikan masukan-masukan inovatif selama penulisan buku ini. Akhirnya kami berharap buku ini menjadi referensi berguna bagi mereka yang membaca

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.

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.

Logika Pemrograman Java Update Version

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Author : Abdul Kadir
language : id
Publisher:
Release Date : 2023-04-18

Logika Pemrograman Java Update Version written by Abdul Kadir and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-18 with categories.

Buku Logika Pemrograman Java ini merupakan salah satu seri dasar pemrograman komputer yang dirancang sebagai bahan penuntun dalam memprogram komputer menggunakan bahasa pemrograman Java. Java adalah bahasa pemrograman yang berorientasi pada objek, bebas platform, dan dikembangkan oleh sun micro system. Java dapat digunakan pada hampir semua bentuk pengembangan software karena memiliki bahasa yang powerful. Beberapa penggunaan Java pada software di antaranya yaitu pembuatan game, aplikasi desktop. aplikasi web, aplikasi jaringan, serta aplikasi enterprise. Java merupakan bahasa pemrograman yang digunakan secara luas untuk pengkodean aplikasi web. Bahasa ini telah menjadi pilihan populer di antara developer selama lebih dari dua dekade, dengan jutaan aplikasi Java yang digunakan saat ini. Dilihat dari penggunaannya, sebagai bahasa pemrograman umum kamu bisa memanfaatkan Java untuk membuat berbagai bentuk aplikasi. Hal itu berlaku mulai dari aplikasi berbasis desktop, website, mobile, hingga aplikasi embedded device seperti perangkat pintar atau mikroprosesor. Java menjadi salah satu bahasa pemrograman terpopuler bukan karena tanpa alasan, bahasa pemrograman ini memiliki beberapa kelebihan seperti misalnya bisa berjalan di sistem operasi yang berbeda-beda. Penggunaan Java menjadi keuntungan bagi programmer karena Java memiliki banyak keunggulan seperti berorientasi pada objek, multiplatform, berbasis GUI, dan dapat digunakan pada aplikasi jaringan terdistribusi. Sinopsis Buku: Buku ini dirancang sebagai bahan penuntun dalam memprogram komputer menggunakan bahasa Java dan dapat digunakan untuk pelajar, mahasiswa, atau siapa saja. Buku ini lebih menekankan pada cara untuk menyelesaikan masalah. Oleh karena itu, banyak contoh permasalahan yang diberikan dan cara untuk menyelesaikannya. Contoh-contoh yang cukup banyak dan bahasa yang mudah dipahami membuat buku ini sangat mudah digunakan dan dapat menjadi penuntun untuk memelajari bahasa Java secara mandiri.

Tutorial Pemrograman Java Untuk Pemula

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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-16

Tutorial Pemrograman Java Untuk Pemula 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 2020-03-16 with Computers categories.

Buku ini memuat sejumlah koleksi contoh – contoh dan latihan – latihan yang menjadi suplemen pembelajaran pemrograman berorientasi-objek di universitas. Pendekatan pada buku ini dipicu oleh kebutuhan mahasiswa dan pelajar dalam menganalisa dan merancang program Java. Material ditulis dan ditulis – kembali sampai para mahasiswa nyaman dengan tiap program yang disajikan. Kebanyakan contoh pada buku ini dihasilkan dari interaksi para mahasiswa di dalam kelas. Buku teks ini didasarkan ide-ide dasar yang dipercaya dapat menjadikan pembaca memiliki kemampuan analisis dan pemrograman berorientasi-objek: § Berorientasi-objek: Buku ini sungguh-sungguh mengajarkan pendekatan berorientasi-objek. Semua pemrosesan program selalu didiskusikan dalam peristilahan berorientasi-objek. Pembaca akan belajar bagaimana menggunakan objek-objek sebelum menulis dan menciptakannya. Buku ini menggunakan pendekatan progresi alamiah yang membuahkan kemampuan dalam merancang solusi-solusi berorientasi-objek. 1. Praktek pemrograman yang benar: Pembaca seharusnya tidak diajari bagaimana memprogram; Pembaca sebaiknya diajari bagaimana menuliskan program yang benar. Buku teks ini mengintegrasikan latihan-latihan yang berperan sebagai fondasi dari keterampilan pemrograman yang baik. Pembaca akan belajar bagaimana menyelesaikan permasalahan dan bagaimana mengimplementasikan solusinya. 2. Contoh: Pembaca akan belajar dari contoh. Buku teks ini diisi dengan contoh-contoh yang diimplementasikan secara utuh untuk mendemonstrasikan konsep-konsep pemrograman yang baik. 3. Grafika dan GUI: Grafika dapat menjadi motivator bagi pembaca, dan kegunaannya dapat berperan sebagai contoh-contoh yang baik untuk pemrograman berorientasi-objek. 4. Bank Soal: Pembaca ditantang untuk menyelesaikan soal-soal yang disediakan secara khusus pada bab Bank Soal.

Pemrograman Java Mulai Dari Nol Sampai Master

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Author : Vivian Siahaan
language : id
Publisher: Sparta Publisher
Release Date : 2018-11-10

Pemrograman Java Mulai Dari Nol Sampai Master written by Vivian Siahaan and has been published by Sparta Publisher this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-10 with Computers categories.

Puji syukur kepada Tuhan Yang Maha Kuasa atas tuntasnya penulisan buku ini. Semua konten di dalam buku ini merupakan pengembangan bahan ajar matakuliah “PEMROGRAMAN BERORIENTASI-OBJEK” selama penulis menjadi pengasuh matakuliah tersebut. Hal lain yang memungkinkan selesainya buku ini adalah deretan diskusi kritis dengan kalangan mahasiswa dan alumni yang memiliki ikatan atau ketertarikan khusus pada bidang pemrograman JAVA. Tanpa semangat muda mereka yang menularkan energi dinamis kepada penulis, mustahil buku ini bisa terealisasi. Buku yang dikhususkan bagi pembaca yang benar-benar ingin menguasai fondasi PBO. Karena fondasi harus kokoh, buku ini sungguh-sungguh memperdalam konsep-konsep yang mendasari PBO misalnya pewarisan dan polimorfisme, overloading metode, dan enkapsulasi. Buku ini ditulis karena spirit untuk mendokumentasikan gagasan-gagasan pemrograman berorientasi objek di dalam keluarga besar JAVA. Di Indonesia, sangat jarang ditemui buku yang mendiskusikan pemrograman JAVA yang mengupas secara detil kelebihan dan kekurangan suatu kode sumber. Buku ini menelaah suatu kode sumber dengan memberikan perhatian khusus terhadap potongan-potongan kode yang dianggap penting. Buku ini dikhususkan bagi mahasiswa sarjana dan pembelajar mandiri yang menjadi pemrogram aktif. Penulis mengucapkan penghargaan yang tinggi kepada Prof. Miike, Dr. Nomura, dan Dr. Osa di Universitas Yamaguchi dan di Universitas Hiroshima yang telah memberikan masukan-masukan inovatif selama penulisan buku ini. Akhirnya kami berharap buku ini menjadi referensi berguna bagi mereka yang membaca. Dengan ini pula, kami menyatakan bahwa semua kesalahan yang ada pada buku ini adalah milik kami.

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.

Introduction To Finite Element Analysis Using Matlab And Abaqus

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Author : Amar Khennane
language : en
Publisher: CRC Press
Release Date : 2013-06-10

Introduction To Finite Element Analysis Using Matlab And Abaqus written by Amar Khennane and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-10 with Technology & Engineering categories.

There are some books that target the theory of the finite element, while others focus on the programming side of things. Introduction to Finite Element Analysis Using MATLAB® and Abaqus accomplishes both. This book teaches the first principles of the finite element method. It presents the theory of the finite element method while maintaining a balance between its mathematical formulation, programming implementation, and application using commercial software. The computer implementation is carried out using MATLAB, while the practical applications are carried out in both MATLAB and Abaqus. MATLAB is a high-level language specially designed for dealing with matrices, making it particularly suited for programming the finite element method, while Abaqus is a suite of commercial finite element software. Includes more than 100 tables, photographs, and figures Provides MATLAB codes to generate contour plots for sample results Introduction to Finite Element Analysis Using MATLAB and Abaqus introduces and explains theory in each chapter, and provides corresponding examples. It offers introductory notes and provides matrix structural analysis for trusses, beams, and frames. The book examines the theories of stress and strain and the relationships between them. The author then covers weighted residual methods and finite element approximation and numerical integration. He presents the finite element formulation for plane stress/strain problems, introduces axisymmetric problems, and highlights the theory of plates. The text supplies step-by-step procedures for solving problems with Abaqus interactive and keyword editions. The described procedures are implemented as MATLAB codes and Abaqus files can be found on the CRC Press website.

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.