Neural Networks From Scratch In Python Amazon
Download Neural Networks From Scratch In Python Amazon PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Neural Networks From Scratch In Python Amazon book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Learn From Scratch Backpropagation Neural Networks Using Python GUI & MariaDB
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.
AWS Certified Machine Learning Study Guide
Author: Shreyas Subramanian
language: en
Publisher: John Wiley & Sons
Release Date: 2021-11-19
Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. You’ll also find: An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning.
Step By Step Neural Networks for Image Classification using Python GUI
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.