Download or read book Step By Step Neural Networks for Image Classification using Python GUI written by Hamzan Wadi and published by Turida Publisher. This book was released on with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a practical explanation of the backpropagation neural networks algorithm and how it can be implemented for image classification. The discussion in this book is presented in step by step so that it will help readers understand the fundamental of the backpropagation neural networks and its steps. This book is very suitable for students, researchers, and anyone who want to learn and implement the backpropagation neural networks for image classification using PYTHON GUI. The discussion in this book will provide readers deep understanding about the backpropagation neural networks architecture and its parameters. The readers will be guided to understand the steps of the backpropagation neural networks for image classification through case example. The readers will be guided to create their own neural networks class and build their complete applications for data image classification. The final objective of this book is that the readers are able to realize each step of the multilayer perceptron neural networks for image classification. In Addition, the readers also are able to create the neural networks applications which consists of two types of applications which are command window based application and GUI based application. Here are the material that you will learn in this book. CHAPTER 1: This chapter will guide you in preparing what software are needed to realize the backpropagation neural networks using Python GUI. The discussion in this chapter will start from installing Python and the libraries that will be used, installing Qt Designer, understanding and using Qt Designer to design the application UI, and the last is about how to create a GUI program using Python and Qt Designer. CHAPTER 2: This chapter discusses the important parts in the backpropagation neural networks algorithm which includes the architecture of the backpropagation neural networks, the parameters contained in the backpropagation neural networks, the steps of the backpropagation neural networks algorithm, and the mathematical calculations of the backpropagation neural networks. CHAPTER 3: This chapter discusses in detail the mathematical calculations of fruit quality classification using the backpropagation neural networks which includes the feature extraction process of fruit images, data normalization, the training process, and the classification process. The feature extraction method used in this case is GLCM (Gray Level Co-occurrence Matrix). The image features that will be used in this case are energy, contrast, entropy, and homogeneity. CHAPTER 4: This chapter discusses how to implement the backpropagation neural networks algorithm for fruit quality classification using Python. This chapter will present the steps to create your backpropagation neural networks class and to define the functions that represent each process of the backpropagation neural networks. This chapter will also present the steps to create a class for image processing. And in final discussion you will be guided to create your backpropagation neural networks application from scratch to classify the quality of fruit. CHAPTER 5: This chapter will discuss how to create a GUI based application for fruit quality classification using the backpropagation neural networks algorithm. This chapter will discuss in detail the steps for designing the application UI by using Qt Designer, the steps for creating a class for the backpropagation neural networks GUI based application, and how to run the GUI based application to classify the fruit data.

Download or read book Step by Step Tutorials on Deep Learning Using Scikit Learn Keras and Tensorflow with Python GUI written by Rismon Hasiholan Sianipar and published by Independently Published. This book was released on 2021-04-24 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion.In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT).In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https: //www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose.In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https: //www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose.In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https: //www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose.In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https: //www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purp

Download or read book Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit Learn Keras and TensorFlow with PYTHON GUI written by Vivian Siahaan and published by . This book was released on 2021-06-03 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book implements deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship dataset provided by Kaggle (https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar).

Download or read book Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit Learn Keras And TensorFlow with PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-21 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). Here's an overview of the steps involved in classifying monkey species using the 10 Monkey Species dataset: Dataset Preparation: Download the 10 Monkey Species dataset from Kaggle and extract the files. The dataset should consist of separate folders for each monkey species, with corresponding images.; Load and Preprocess Images: Use libraries such as OpenCV to load the images from the dataset. Resize the images to a consistent size (e.g., 224x224 pixels) to ensure uniformity.; Split the Dataset: Divide the dataset into training and testing sets. Typically, an 80:20 or 70:30 split is used, where the larger portion is used for training and the smaller portion for testing the model's performance.; Label Encoding: Encode the categorical labels (monkey species) into numeric form. This step is necessary to train a machine learning model, as most algorithms expect numerical inputs.; Feature Extraction: Extract meaningful features from the images using techniques like deep learning or image processing algorithms. This step helps in representing the images in a format that the machine learning model can understand.; Model Training: Use libraries like TensorFlow and Keras to train a machine learning model on the preprocessed data. Choose an appropriate model architecture, in this case, MobileNetV2.; Model Evaluation: Evaluate the trained model on the testing set to assess its performance. Metrics like accuracy, precision, recall, and F1-score can be used to evaluate the model's classification performance.; Predictions: Use the trained model to make predictions on new, unseen images. Pass the images through the trained model and obtain the predicted labels for the monkey species. In chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). Here's the outline of the steps: Step 1: Dataset Preparation: Download the rock-paper-scissors dataset from Kaggle by visiting the provided link and clicking on the "Download" button. Save the dataset to a local directory on your machine. Extract the downloaded dataset to a suitable location. This will create a folder containing the images for rock, paper, and scissors.; Step 2: Data Preprocessing: Import the required libraries: TensorFlow, Keras, NumPy, OpenCV, and Pandas. Load the dataset using OpenCV: Iterate through the image files in the dataset directory and use OpenCV's cv2.imread() function to load each image. You can specify the image's file extension (e.g., PNG) and directory path. Preprocess the images: Resize the loaded images to a consistent size using OpenCV's cv2.resize() function. You may choose a specific width and height suitable for your model. Prepare the labels: Create a list or array to store the corresponding labels for each image (rock, paper, or scissors). This can be done based on the file naming convention or by mapping images to their respective labels using a dictionary.; Step 3: Model Training: Create a convolutional neural network (CNN) model using Keras: Define a CNN architecture using Keras' Sequential model or functional API. This typically consists of convolutional layers, pooling layers, and dense layers. Compile the model: Specify the loss function (e.g., categorical cross-entropy) and optimizer (e.g., Adam) using Keras' compile() function. You can also define additional metrics to evaluate the model's performance. Train the model: Use Keras' fit() function to train the model on the preprocessed dataset. Specify the training data, labels, batch size, number of epochs, and validation data if available. This will optimize the model's weights based on the provided dataset. Save the trained model: Once the model training is complete, you can save the trained model to disk using Keras' save() or save_weights() function. This allows you to load the model later for predictions or further training.; Step 4: Model Evaluation: Evaluate the trained model: Use Keras' evaluate() function to assess the model's performance on a separate testing dataset. Provide the testing data and labels to calculate metrics such as accuracy, precision, recall, and F1 score. This will help you understand how well the model generalizes to new, unseen data. Analyze the model's performance: Interpret the evaluation metrics and analyze any potential areas of improvement. You can also visualize the confusion matrix or classification report to gain more insights into the model's predictions.; Step 5: Prediction: Use the trained model for predictions: Load the saved model using Keras' load_model() function. Then, pass new, unseen images through the model to obtain predictions. Preprocess these images in the same way as the training images (resize, normalize, etc.). Visualize and interpret predictions: Display the predicted labels alongside the corresponding images to see how well the model performs. You can use libraries like Matplotlib or OpenCV to show the images and their predicted labels. Additionally, you can calculate the accuracy of the model's predictions on the new dataset. In chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship dataset provided by Kaggle (https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar). Here are the outline steps: Import the required libraries: TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy. Load and preprocess the dataset: Read the images from the dataset folder. Resize the images to a fixed size. Store the images and corresponding labels.; Split the dataset into training and testing sets: Split the data and labels into training and testing sets using a specified ratio.; Encode the labels: Convert the categorical labels into numerical format. Perform one-hot encoding on the labels.; Build MobileNetV2 model using Keras: Create a sequential model. Add convolutional layers with activation functions. Add pooling layers for downsampling. Flatten the output and add dense layers. Set the output layer with softmax activation.; Compile and train the model: Compile the model with an optimizer and loss function. Train the model using the training data and labels. Specify the number of epochs and batch size.; Evaluate the model: Evaluate the trained model using the testing data and labels. Calculate the accuracy of the model.; Make predictions on new images: Load and preprocess a new image. Use the trained model to predict the label of the new image. Convert the predicted label from numerical format to categorical.

Download or read book Step By Step Database Programming using Python GUI MySQL written by Hamzan Wadi and published by TURIDA Publisher. This book was released on with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a practical explanation of database programming using Python GUI & MySQL. The discussion in this book is presented in step by step so that it will help readers understand each material and also will make it easier for the readers to follow all of the instructions. This book is very suitable for students, programmers, and anyone who want to learn database programming using Python GUI & MySQL from scratch. This book is divided into two parts: The first part of this book will discuss about the fundamentals of database programming using Python GUI & MySQL. This part will discuss in detail about how to setup your working environment and how to understand GUI programming using Python. This part will also discuss in detail about how to start your database programming using Python GUI & MySQL. This part will discuss in detail about the basic of database programming using Python GUI & MySQL. The second part of this book will discuss about how to build database application using Python GUI & MySQL. This part will discuss in detail about how to build Multiple Document Interface (MDI) database application through real project-based example. This part will discuss in detail about how to design and create database for Library Management System application, and how to create all forms for the application. The final objective of this book is that the readers are able to create real database application using Python GUI & MySQL. Here are the materials that you will learn in this book. PART I: THE FUNDAMENTAL OF DATABASE PROGRAMMING USING PYTHON GUI & MySQL CHAPTER 1: The discussion in this chapter will guide you in preparing what software are needed to start your database programming using Python GUI. This chapter will guide you to install all software including Python, MySQL, and Qt Designer. In addition, this chapter also will discuss about how to understand and use Qt Designer for user interface design, and how to create a GUI application using Python and Qt Designer. CHAPTER 2: The discussion in this chapter will guide you to start your database programming using Python GUI & MySQL. This chapter will discuss in detail about the basic of database programming using Python GUI & MySQL. The discussion in this chapter will talk about how to create and drop database, how to create and drop table, how to insert data into table, how to display data from table, how to update data in table, and how to delete data in table. All discussions in this chapter will give you deep understanding of database programming using Python GUI & MySQL. PART II: BUILDING DATABASE APPLICATION USING PYTHON GUI & MySQL, CASE STUDY: LIBRARY MANAGEMENT SYSTEM APPLICATION CHAPTER 3: The discussion in this chapter will guide you to design and create database for library management system application. This is the first step that must be taken to create database application using Python GUI & MySQL. This chapter will discuss in detail about how to design the Entity Relationship Diagram (ERD) for library management system application. The discussion in this chapter will also talk about how to create database and its tables based on the ERD design using MySQL server. CHAPTER 4: The discussion in this chapter will guide you to create main form and login form for the application. This chapter will discuss in detail about how to create these two forms. These forms are the first two forms that we will create in building library management system application. This chapter will also discuss about how to run the application. CHAPTER 5: The discussion in this chapter will guide you to create user accounts form and members form for Library Management System application. This chapter will discuss in detail about how to create these two forms. This chapter will also discuss about how to add these two forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use the forms to manage user accounts and members data of Library Management System application. CHAPTER 6: The discussion in this chapter will guide you to create authors form, genres form, and books form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add books form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage authors, genres, and books data in Library Management System application. CHAPTER 7: The discussion in this chapter will guide you to create member search form, book search form, and loan transaction form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add loan transaction form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage loan transactions in Library Management System application. CHAPTER 8: The discussion in this chapter will guide you to create members statistic form, books statistic form, and loan statistic form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add all of the forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use all of the forms to display the statistics in the library.

Download or read book Learn From Scratch Backpropagation Neural Networks Using Python GUI MariaDB written by Hamzan Wadi and published by Turida Publisher. This book was released on with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a practical explanation of the backpropagation neural networks and how it can be implemented for data prediction and data classification. The discussion in this book is presented in step by step so that it will help readers understand the fundamental of the backpropagation neural networks and its steps. This book is very suitable for students, researchers, and anyone who want to learn and implement the backpropagation neural networks for data prediction and data classification using PYTHON GUI and MariaDB. The discussion in this book will provide readers deep understanding about the backpropagation neural networks architecture and its parameters. The readers will be guided to understand the steps of the backpropagation neural networks for data prediction and data classification through case examples. In addition, readers are also guided step by step to implement the backpropagation neural networks for data prediction and data classification using PYTHON GUI and MariaDB. The readers will be guided to create their own backpropagation neural networks class and build their complete applications for data prediction and data classification. This book consists of three cases which are realized into complete projects using the Python GUI and MariaDB. The three cases that will be learned in this book are as follow. 1. Sales prediction using the backpropagation neural networks. 2. Earthquake data prediction using the backpropagation neural networks. 3. Fruit quality classification using the backpropagation neural networks. Each case in this book is equipped with a mathematical calculation that will help the reader understand each step that must be taken. The cases in this book are realized into three types of applications which are command window based application, GUI based application, and database application using Python GUI and MariaDB. The final result of this book is that the readers are able to realize each step of the backpropagation neural networks for data prediction and data classification. In Addition, the readers also are able to create the backpropagation neural networks applications which consists of three types of applications which are command window based application, GUI based application, and database application using Python GUI and MariaDB.

Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Download or read book The Practical Guides On Deep Learning Using SCIKIT LEARN KERAS and TENSORFLOW with Python GUI written by Rismon Hasiholan Sianipar and published by . This book was released on 2021-04-11 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 datasetIn Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram.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 with PyQt. You will build a GUI application for this purpose.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 provided by Kaggle (https: //www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. 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 (https: //www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose.In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https: //www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpo

Download or read book Hands On Guide To IMAGE CLASSIFICATION Using Scikit Learn Keras And TensorFlow with PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-20 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, implement deep learning on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). Here's an overview of the steps involved in detecting face masks using the Face Mask Detection Dataset: Import the necessary libraries: Import the required libraries like TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, and NumPy.; Load and preprocess the dataset: Load the dataset and perform any necessary preprocessing steps, such as resizing images and converting labels into numeric representations.; Split the dataset: Split the dataset into training and testing sets using the train_test_split function from Scikit-Learn. This will allow us to evaluate the model's performance on unseen data.; Data augmentation (optional): Apply data augmentation techniques to artificially increase the size and diversity of the training set. Techniques like rotation, zooming, and flipping can help improve the model's generalization.; Build the model: Create a Convolutional Neural Network (CNN) model using TensorFlow and Keras. Design the architecture of the model, including the number and type of layers.; Compile the model: Compile the model by specifying the loss function, optimizer, and evaluation metrics. This prepares the model for training. Train the model: Train the model on the training dataset. Adjust the hyperparameters, such as the learning rate and number of epochs, to achieve optimal performance.; Evaluate the model: Evaluate the trained model on the testing dataset to assess its performance. Calculate metrics such as accuracy, precision, recall, and F1 score.; Make predictions: Use the trained model to make predictions on new images or video streams. Apply the face mask detection algorithm to identify whether a person is wearing a mask or not.; Visualize the results: Visualize the predictions by overlaying bounding boxes or markers on the images or video frames to indicate the presence or absence of face masks. In chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). To classify weather using the Multi-class Weather Dataset from Kaggle, you can follow these general steps: Load the dataset: Use libraries like Pandas or NumPy to load the dataset into memory. Explore the dataset to understand its structure and the available features.; Preprocess the data: Perform necessary preprocessing steps such as data cleaning, handling missing values, and feature engineering. This may include resizing images (if the dataset contains images) or encoding categorical variables.; Split the data: Split the dataset into training and testing sets. The training set will be used to train the model, and the testing set will be used for evaluating its performance.; Build a model: Utilize TensorFlow and Keras to define a suitable model architecture for weather classification. The choice of model depends on the type of data you have. For image data, convolutional neural networks (CNNs) often work well.; Train the model: Train the model using the training data. Use appropriate training techniques like gradient descent and backpropagation to optimize the model's weights.; Evaluate the model: Evaluate the trained model's performance using the testing data. Calculate metrics such as accuracy, precision, recall, or F1-score to assess how well the model performs.; Fine-tune the model: If the model's performance is not satisfactory, you can experiment with different hyperparameters, architectures, or regularization techniques to improve its performance. This process is called model tuning.; Make predictions: Once you are satisfied with the model's performance, you can use it to make predictions on new, unseen data. Provide the necessary input (e.g., an image or weather features) to the trained model, and it will predict the corresponding weather class. In chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download). Here are the general steps involved in recognizing flowers: Data Preparation: Download the Flowers Recognition dataset from Kaggle and extract the contents. Import the required libraries and define the dataset path and image dimensions.; Loading and Preprocessing the Data: Load the images and their corresponding labels from the dataset. Resize the images to a specific dimension. Perform label encoding on the flower labels and split the data into training and testing sets. Normalize the pixel values of the images.; Building the Model: Define the architecture of your model using TensorFlow's Keras API. You can choose from various neural network architectures such as CNNs, ResNet, or InceptionNet. The model architecture should be designed to handle image inputs and output the predicted flower class..; Compiling and Training the Model: Compile the model by specifying the loss function, optimizer, and evaluation metrics. Common choices include categorical cross-entropy loss and the Adam optimizer. Train the model using the training set and validate it using the testing set. Adjust the hyperparameters, such as the learning rate and number of epochs, to improve performance.; Model Evaluation: Evaluate the trained model on the testing set to measure its performance. Calculate metrics such as accuracy, precision, recall, and F1-score to assess how well the model is recognizing flower classes.; Prediction: Use the trained model to predict the flower class for new images. Load and preprocess the new images in a similar way to the training data. Pass the preprocessed images through the trained model and obtain the predicted flower class labels.; Further Improvements: If the model's performance is not satisfactory, consider experimenting with different architectures, hyperparameters, or techniques such as data augmentation or transfer learning. Fine-tuning the model or using ensembles of models can also improve accuracy.

Download or read book In Depth Tutorials Deep Learning Using Scikit Learn Keras and TensorFlow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2021-06-05 with total page 1459 pages. Available in PDF, EPUB and Kindle. Book excerpt: BOOK 1: LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI 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: 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. BOOK 2: THE PRACTICAL GUIDES ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI 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 line graph using PyQt. You will also learn how to display image and its histogram. 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 with PyQt. You will build a GUI application for this purpose. 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 provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. 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 (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. BOOK 3: STEP BY STEP TUTORIALS ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. BOOK 4: Project-Based Approach On DEEP LEARNING Using Scikit-Learn, Keras, And TensorFlow with Python GUI In this book, implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download). BOOK 5: Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download). BOOK 6: Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship dataset provided by Kaggle (https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar).

Download or read book Step by Step Tutorials On Deep Learning Using Scikit Learn Keras and Tensorflow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-18 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. Here's the outline of the steps, focusing on transfer learning: 1. Dataset Preparation: Download the Fruits 360 dataset from Kaggle. Extract the dataset files and organize them into appropriate folders for training and testing. Install the necessary libraries like TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, and NumPy; Data Preprocessing: Use OpenCV to read and load the fruit images from the dataset. Resize the images to a consistent size to feed them into the neural network. Convert the images to numerical arrays using NumPy. Normalize the image pixel values to a range between 0 and 1. Split the dataset into training and testing sets using Scikit-Learn. 3. Building the Model with Transfer Learning: Import the required modules from TensorFlow and Keras. Load a pre-trained model (e.g., VGG16, ResNet50, InceptionV3) without the top (fully connected) layers. Freeze the weights of the pre-trained layers to prevent them from being updated during training. Add your own fully connected layers on top of the pre-trained layers. Compile the model by specifying the loss function, optimizer, and evaluation metrics; 4. Model Training: Use the prepared training data to train the model. Specify the number of epochs and batch size for training. Monitor the training process for accuracy and loss using callbacks; 5. Model Evaluation: Evaluate the trained model on the test dataset using Scikit-Learn. Calculate accuracy, precision, recall, and F1-score for the classification results; 6. Predictions: Load and preprocess new fruit images for prediction using the same steps as in data preprocessing. Use the trained model to predict the class labels of the new images. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. The following steps are taken: Set up your development environment: Install the necessary libraries such as TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, and any other dependencies required for the tutorial; Load and preprocess the dataset: Use libraries like OpenCV and NumPy to load and preprocess the dataset. Split the dataset into training and testing sets; Design and train the classification model: Use TensorFlow and Keras to design a convolutional neural network (CNN) model for image classification. Define the architecture of the model, compile it with an appropriate loss function and optimizer, and train it using the training dataset; Evaluate the model: Evaluate the trained model using the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Preprocess the images, feed them into the model, and obtain the predicted class labels; Visualize the results: Use libraries like Matplotlib or OpenCV to visualize the results, such as displaying sample images with their predicted labels, plotting the training/validation loss and accuracy curves, and creating a confusion matrix. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. Here are the steps you can follow to perform furniture detection: Dataset Preparation: Extract the dataset files and organize them into appropriate directories for training and testing; Data Preprocessing: Load the dataset using Pandas to analyze and preprocess the data. Explore the dataset to understand its structure, features, and labels. Perform any necessary preprocessing steps like resizing images, normalizing pixel values, and splitting the data into training and testing sets; Feature Extraction and Representation: Use OpenCV or any image processing libraries to extract meaningful features from the images. This might include techniques like edge detection, color-based features, or texture analysis. Convert the images and extracted features into a suitable representation for machine learning models. This can be achieved using NumPy arrays or other formats compatible with the chosen libraries; Model Training: Define a deep learning model using TensorFlow and Keras for furniture detection. You can choose pre-trained models like VGG16, ResNet, or custom architectures. Compile the model with an appropriate loss function, optimizer, and evaluation metrics. Train the model on the preprocessed dataset using the training set. Adjust hyperparameters like batch size, learning rate, and number of epochs to improve performance; Model Evaluation: Evaluate the trained model using the testing set. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance. Analyze the results and identify areas for improvement; Model Deployment and Inference: Once satisfied with the model's performance, save it to disk for future use. Deploy the model to make predictions on new, unseen images. Use the trained model to perform furniture detection on images by applying it to the test set or new data. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. Here are the general steps to implement image classification using the Fashion MNIST dataset: Import the necessary libraries: Import the required libraries such as TensorFlow, Keras, NumPy, Pandas, and Matplotlib for handling the dataset, building the model, and visualizing the results; Load and preprocess the dataset: Load the Fashion MNIST dataset, which consists of images of clothing items. Split the dataset into training and testing sets. Preprocess the images by scaling the pixel values to a range of 0 to 1 and converting the labels to categorical format; Define the model architecture: Create a convolutional neural network (CNN) model using Keras. The CNN consists of convolutional layers, pooling layers, and fully connected layers. Choose the appropriate architecture based on the complexity of the dataset; Compile the model: Specify the loss function, optimizer, and evaluation metric for the model. Common choices include categorical cross-entropy for multi-class classification and Adam optimizer; Train the model: Fit the model to the training data using the fit() function. Specify the number of epochs (iterations) and batch size. Monitor the training progress by tracking the loss and accuracy; Evaluate the model: Evaluate the trained model using the test dataset. Calculate the accuracy and other performance metrics to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Load the test images, preprocess them, and pass them through the model to obtain class probabilities or predictions; Visualize the results: Visualize the training progress by plotting the loss and accuracy curves. Additionally, you can visualize the predictions and compare them with the true labels to gain insights into the model's performance.

Download or read book Deep Learning written by Frank Millstein and published by Frank Millstein. This book was released on 2020-08-14 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning - 2 BOOK BUNDLE!! Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks. Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book bundle NOW and SAVE money!

Download or read book Applied Artificial Intelligence written by Wolfgang Beer and published by Wolfgang Beer. This book was released on with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: About This Book Step into the amazing world of Artificial Intelligence and Machine Learning using this compact and easy to understand book. Dive into Neural Networks and Deep Learning and create your own production ready AI models by using TensorFlow and Keras. Work through simple yet insightful examples that will get you up and running with Artificial Intelligence, TensorFlow and Keras in no time. Who This Book Is For This book is for Python developers who want to understand Neural Networks from ground up and build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. What You Will Learn The basic structure and functionality of a Neuron The basic math behind the Neural Network learning process See how to build a simple character recognition model from ground up What classification, regression and clustering is How to use TensorFlow to build production ready models Build a first model with the Keras framework How to predict the survival chance for Titanic passengers How to build a simple book recommender How to detect toxic language with an AI model In Detail Artificial Intelligence became one of the hottest topics in the modern economy, where everything is driven by software, network and data. There exists nearly no startup nor traditional business where Artificial Intelligence is not used extensively across many fields such as search engines, image recognition, robotics or finance. This book gives a ground up, step by step introduction about how a Neural Network is used to learn a given function and to make intelligent data-driven decisions. The book explains how to identify typical use-cases such as classification, regression and clustering in terms of practical and well known use-cases. This book comes with an introduction into the state-of-the-art Google TensorFlow framework that allows developers to roll out their models in production. On top of TensorFlow, the Keras library is used to simplify the design and training of complex deep-learning models. This book comes with multiple examples that show how to apply Artificial Intelligence and Machine Learning models for use-cases such as handwriting recognition, decision making, text analysis and toxic comment identification as well as the use of AI to recommend products to customers.

Download or read book Convolutional Neural Networks In Python written by Frank Millstein and published by Frank Millstein. This book was released on 2020-07-06 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book NOW and learn more about Convolutional Neural Networks in Python!

Download or read book Programming With Python written by Frank Millstein and published by Frank Millstein. This book was released on 2020-09-05 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Programming With Python - 4 BOOK BUNDLE!! Deep Learning with Keras Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Convolutional Neural Networks in Python Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Python Machine Learning Here Is A Preview Of What You’ll Learn Here… Basics behind machine learning techniques Different machine learning algorithms Fundamental machine learning applications and their importance Getting started with machine learning in Python, installing and starting SciPy Loading data and importing different libraries Data summarization and data visualization Evaluation of machine learning models and making predictions Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests Solving multi-clasisfication problems Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn Solving multi-label classification problems And much, much more... Machine Learning With TensorFlow Here Is a Preview of What You’ll Learn Here… What is machine learning Main uses and benefits of machine learning How to get started with TensorFlow, installing and loading data Data flow graphs and basic TensorFlow expressions How to define your data flow graphs and how to use TensorBoard for data visualization Main TensorFlow operations and building tensors How to perform data transformation using different techniques How to build high performance data pipelines using TensorFlow Dataset framework How to create TensorFlow iterators Creating MNIST classifiers with one-hot transformation Get this book bundle NOW and SAVE money!

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

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