Название: Analysis and Visualization of Discrete Data Using Neural Networks
Автор: Коji Коуаmаdа
Издательство: World Scientific Publishing
Год: 2024
Страниц: 230
Язык: английский
Формат: pdf (true)
Размер: 53.9 MB

This book serves as a comprehensive step-by-step guide on data analysis and statistical analysis. It covers fundamental operations in Excel, such as table components, formula bar, and ribbon, and introduces visualization techniques and PDE derivation using Excel. It also provides an overview of Google Colab, including code and text cells, and explores visualization and Deep Learning applications. Key features of the book include topics like statistical analysis, regression analysis, optimization, correlation analysis, and neural networks. It adopts a practical approach by providing examples and step-by-step instructions for learners to apply the techniques to real-world problems. The book also highlights the strengths and features of both Excel and Google Colab, allowing learners to leverage the capabilities of each platform. The clear explanations of concepts, visual aids, and code snippets aid comprehension help learners understand the principles of data analysis and statistical analysis. Overall, this book serves as a valuable resource for professionals, researchers, and students seeking to develop skills in data analysis, regression statistics, optimization, and advanced modeling techniques using Excel, Colab, and neural networks. Google Colaboratory (Colab for short) is a free cloud service provided by Google, where you can use the Jupyter notebook and Python to analyze data and build a model for Machine Learning.

Название: Intergalactic Python: Coding Through the Cosmos Learn Python the exciting way
Автор: Andrew Kean Gao
Издательство: Leanpub
Год: 2023-07-16
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Greetings, future space explorer! You're about to embark on a journey unlike any other. This isn't just another Python book; it's a voyage of discovery through the cosmos. This book will guide you from the ground up, from your first tentative steps in Python to a confident sprint across the star-studded fields of coding. If you've always been captivated by the mysteries of the universe and wanted to learn to code, you've picked up the right guide! This book assumes no prior programming knowledge. We'll be learning the Python programming language, known for its simplicity and power. Python is widely used in many fields, from web development to data analysis to machine learning, making it a valuable language to learn. We'll start with the basics, like how to write and run Python programs. Gradually, we'll delve into Python's core concepts like variables, loops, conditionals, and functions. Later, we'll explore more advanced topics like error handling, libraries, and object-oriented programming. By the end, you'll have a solid foundation in Python and be able to write your own Python programs. We will be using Google Colab, a free online platform, as our Integrated Development Environment (IDE). It allows you to write, run, and share Python code right in your browser, which means there's no software to install and you can access it from anywhere.

Название: Machine Learning for High-Risk Applications: Approaches to Responsible AI (Final)
Автор: Patrick Hall, James Curtis, Parul Pandey
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 469
Язык: английский
Формат: True PDF, True EPUB (Retail Copy)
Размер: 42.7 MB

The past decade has witnessed the broad adoption of Artificial Intelligence and Machine Learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.

Название: TensorFlow 2.x in the Colaboratory Cloud: An Introduction to Deep Learning on Google’s Cloud Service
Автор: David Paper
Издательство: Apress
Год: 2021
Страниц: 279
Язык: английский
Формат: pdf (true), rtf, epub
Размер: 10.1 MB

Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab.