Название: Training Data for Machine Learning: Human Supervision from Annotation to Data Science (Seventh release)
Автор: Anthony Sarkis
Издательство: O’Reilly Media, Inc.
Год: 2023-01-26
Страниц: 204
Язык: английский
Формат: epub
Размер: 10.1 MB

Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in Deep Learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. What is Training Data? Training Data is the control of a Supervised System. Training Data controls the system by defining the ground truth goals for the creation of Machine Learning models. This involves technical representations, people decisions, processes, tooling, system design, and a variety of new concepts specific to Training Data. In a sense, a Training Data mindset is a paradigm upon which a growing list of theories, research and standards are emerging. A Machine Learning (ML) Model that is created as the end result of a ML Training Process. The code is written to Python.

Название: Software Requirements Essentials: Core Practices for Successful Business Analysis (Rough Cuts)
Автор: Karl Wiegers, Candase Hokanson
Издательство: Pearson Education
Год: 2023
Страниц: 208
Язык: английский
Формат: epub
Размер: 10.2 MB

20 Best Practices for Developing and Managing Requirements on Any Project. This book describes the twenty most important practices that help software teams create a set of requirements to serve as the foundation for the subsequent development work. These practices broadly apply regardless of the type of product the team is creating or their development approach. Some software teams work not on discrete development projects but on existing products that demand ongoing modifications and new functionality. The people who are responsible for requirements work on product teams like those will find the practices in this book equally applicable to their work.

Название: Introduction to Unity ML-Agents: Understand the Interplay of Neural Networks and Simulation Space Using the Unity ML-Agents Package
Автор: Dylan Engelbrecht
Издательство: Apress
Год: 2023
Страниц: 213
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Demystify the creation of efficient AI systems using the model-based reinforcement learning Unity ML-Agents - a powerful bridge between the world of Unity and Python. We will start with an introduction to the field of AI, then discuss the progression of AI and where we are today. We will follow this up with a discussion of moral and ethical considerations. You will then learn how to use the powerful machine learning tool and investigate different potential real-world use cases. We will examine how AI agents perceive the simulated world and how to use inputs, outputs, and rewards to train efficient and effective neural networks. Next, you'll learn how to use Unity ML-Agents and how to incorporate them into your game or product. This book will thoroughly introduce you to ML-Agents in Unity and how to use them in your next project. Tthose interested in machine learning using Unity ML-Agents. To get the best out of this book, you should have a fundamental understanding of C#, some background in Python, and are well versed in Unity.

Название: Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era
Автор: A. Srinivasan
Издательство: IGI Global
Год: 2023
Страниц: 400
Язык: английский
Формат: epub (true)
Размер: 39.2 MB

This book explores traditional and new areas of the computer vision, Machine Learning and Deep Learning combined to solve a range of problems with the objective to integrate the knowledge of the growing international community of researchers working on the application of Machine Learning and Deep Learning Methods in Vision and Robotics. Image processing is one of the rapidly developing technologies that is spawning important research fields in Engineering discipline. Image Processing refers to the application of algorithms to images meant to improve the quality of the image or to alter it for a different visual effect. It plays a very important role to prepare images for Computer Vision models, such as applying segmentation or labelling known objects. Computer vision and image processing are inseparably linked. A computer vision system receives an image as input and produces task-specific data, such as item labels and coordinates. Deep Learning plays a prominent role in a variety of computer vision problems, such as object detection motion tracking action recognition human pose estimation and semantic segmentation etc.

Название: Deep Learning Research Applications for Natural Language Processing
Автор: L. Ashok Kumar, Dhanaraj Karthika Renuka, S. Geetha
Издательство: IGI Global
Год: 2023
Страниц: 290
Язык: английский
Формат: epub (true)
Размер: 24.1 MB

Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in Artificial Intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as Deep Learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.

Название: MATLAB for Medical Physics: Real-life Clinical Scenarios and Projects
Автор: Jidi Sun
Издательство: Springer
Год: 2023
Страниц: 288
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book gives the practical introduction for medical physics students and clinical physicists to learn MATLAB programming. The first part of the book explains the MATLAB software layout and ways to get help followed by the demonstration of the fundamentals of MATLAB programming through over 100 examples. The second part of the book features eighteen real-life clinical scenarios and projects and twenty-three scenario expansions. The scenarios cover many of the common clinical medical physics areas including DICOM file manipulation, film dosimetry, brachytherapy application, linear accelerator and CT quality assurance and their automations, medical image processing and analysis. All scenarios include the step-by-step solution as a guidance for readers to learn MATLAB by practicing.

Название: Learning Git: A Hands-On Approach to Understanding the Basics of Git (Fifth Early Release)
Автор: Anna Skoulikari
Издательство: O’Reilly Media, Inc.
Год: 2023-01-25
Страниц: 168
Язык: английский
Формат: epub (true), mobi
Размер: 10.1 MB

Learning Git uses storytelling principles not only to place lessons in a familiar context, but also to make the information more memorable. By introducing information in an incremental manner, this book makes all the content accessible so you don't get bogged down with unknown terms or concepts. This guide is ideal whether you're working on a shared document or managing your own version control project. This book is for anyone that wants to learn the basics of how to use Git. It is especially designed for individuals that are newly gaining technical skills or individuals that work in non-technical roles but still need to use Git to collaborate with their technical counterparts. Some examples of individuals that may benefit from this book include but are not limited to: coding bootcamp students, computer science students, technical writers, product managers, designers, junior developers, data scientists, and self-taught programmers.

Название: Machine Learning Algorithms in Depth (MEAP v3)
Автор: Vadim Smolyakov
Издательство: Manning Publications
Год: 2023
Страниц: 186
Язык: английский
Формат: pdf, epub
Размер: 11.97 MB

Machine Learning Algorithms in Depth dives deep into the how and the why of Machine Learning algorithms. For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python. You’ll explore dozens of examples from across all the fields of Machine Learning, including finance, computer vision, NLP, and more. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. By the time you’re done reading, you’ll know how major algorithms work under the hood—and be a better Machine Learning practitioner for it. This book will take you on a journey from mathematical derivation to software implementation of some of the most intriguing algorithms in ML. Some of the prerequisites for reading this book include basic level of programming in Python and intermediate level of understanding of linear algebra, applied probability and multivariate calculus. My goal in writing this book is to distill the science of ML and present it in a way that will convey intuition and inspire the reader to self-learn, innovate and advance the field.

Название: Advanced Machine Learning Algorithms for Complex Financial Applications
Автор: Mohammad Irfan, Mohamed Elhoseny, Salina Kassim
Издательство: IGI Global
Год: 2023
Страниц: 324
Язык: английский
Формат: epub (true)
Размер: 12.9 MB

The advancements in Artificial Intelligence and Machine Learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are becoming more complex within the field of finance. Artificial Intelligence and Machine Learning, with their spectacular success accompanied by unprecedented accuracies, have become increasingly important in the finance world. Advanced Machine Learning Algorithms for Complex Financial Applications provides innovative research on the roles of Artificial Intelligence and Machine Learning algorithms in financial sectors with special reference to complex financial applications such as financial risk management in Big Data environments. In addition, the book addresses broad challenges in both theoretical and application aspects of Artificial Intelligence in the field of finance. Covering essential topics such as secure transactions, financial monitoring, and data modeling, this reference work is crucial for financial specialists, researchers, academicians, scholars, practitioners, instructors, and students.

Название: Bayesian Statistical Modeling With Stan, R, and Python
Автор: Kentaro Matsuura
Издательство: Springer
Год: 2023
Страниц: 395
Язык: английский
Формат: pdf (true), epub
Размер: 41.5 MB

This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world dаta: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.