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.

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.

KEY FEATURES ● Understand applications like reinforcement learning, automatic driving and image generation. ● Understand neural networks accompanied with figures and charts. ● Learn about determining coefficients and initial values of weights. DESCRIPTION Deep learning helps you solve issues related to data problems as it has a vast array of mathematical algorithms and has capacity to detect patterns. This book starts with a quick view of deep learning in Python which would include definition, features and applications. You would be learning about perceptron, neural networks, Backpropagation. This book would also give you a clear insight of how to use Numpy and Matplotlin in deep learning models. By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning. WHAT YOU WILL LEARN ● To develop deep learning applications, use Python with few outside inputs. ● Study several ideas of profound learning and neural networks ● Learn how to determine coefficients of learning and weight values ● Explore applications such as automation, image generation and reinforcement learning ● Implement trends like batch Normalisation, dropout, and Adam WHO THIS BOOK IS FOR Deep Learning from the Basics is for data scientists, data analysts and developers who wish to build efficient solutions by applying deep learning techniques. Individuals who would want a better grasp of technology and an overview. You should have a workable Python knowledge is a required. NumPy knowledge and pandas will be an advantage, but that’s completely optional. TABLE OF CONTENTS 1. Python Introduction 2. Perceptron in Depth 3. Neural Networks 4. Training Neural Network 5. Backpropagation 6. Neural Network Training Techniques 7. CNN 8. Deep Learning

In this book, you will create two MariaDB and PostgreSQL driven projects using PyQt. The step-by-step guide in this book is expected to help the reader's confidence to become a programmer who can solve database programming problems. A progressive project is provided to demonstrate how to apply the concepts of MariaDB and PostgreSQL using Python. In second chapter, you will learn PyQt that consists of a number of Python bindings for cross-platform applications that combine all the strengths of Qt and Python. By using PyQt, you can include all Qt libraries in Python code, so you can write GUI applications in Python. In other words, you can use PyQt to access all the features provided by Qt through Python code. Because PyQt depends on the Qt libraries at run time, you need to install PyQt. In third chapter, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In fourth chapter, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In this chapter, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter five, you will create dan configure PotgreSQL database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter six, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have a VARCHAR data type (200). You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In chapter eight, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables as well.

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Become A Deep Learning Expert With Python! Machine Learning is probably the most important and fastest-growing industry in today's world. It's a much hyped term and nowadays it can be found almost everywhere. In robots, video games, the stock market, home appliances and even in cars. Most of the biggest advancements are made with Neural Networks! These are complex machine learning structures that are inspired by the human brain! The people who don't educate themselves on this matter will be overrun by the development instead of being a part of it. In this sixth volume of The Python Bible, you will learn deep learning in Python from scratch. We will start with the theoretical understanding of Neural Networks and basic algorithms like Gradient Descent or Backpropagation. Then we will work on awesome projects like recognizing handwritten digits, generating poetic texts and recognizing objects on images! After Reading This Book You Will Have The Following Skills: - Deep Understanding of Neural Networks- Recognizing Handwritten Digits Using Machine Learning- Generating Poetic d104s with Neural Networks- Predicting Sequential Data (Stocks, Weather etc.) with Recurrent Neural Networks- Processing Audio and Video Data- Recognizing Objects Like Horses, Cars and Trucks on Images- Understanding Recurrent Neural Networks- Understanding Convolutional Neural Networks Master Deep Learning With Python!

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