Tiny Stories Dataset
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Telling Stories with Data
The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way. At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout.
Proceedings of the Fifth International Conference on Trends in Computational and Cognitive Engineering
This book presents various computational and cognitive modeling approaches in the areas of health, education, finance, environment, engineering, commerce and industry. It is a collection of selected conference papers presented at the 5th International Conference on Trends in Cognitive Computation Engineering (TCCE 2023), organized by Pranveer Singh Institute of Technology, Kanpur Uttar Pradesh, India in collaboration with IIOIR, Shimla, Himachal Pradesh, India, during 24 – 25 November 2023. The book is divided into two volumes, and it shares cutting-edge insights and ideas from mathematicians, engineers, scientists, and researchers and discusses fresh perspectives on problem solving in a range of research areas.
New Kind of Machine Learning–Cellular Automata Model
Author: Parimal Pal Chaudhuri
language: en
Publisher: Springer Nature
Release Date: 2025-04-25
This book introduces the CAML model, a novel integration of Cellular Automata (CA) and Machine Learning (ML), designed to deliver efficient computation with minimal training data and low computing resources. CAML operates through two key perspectives: one where CA is enhanced by ML to handle complex non-linear evolution, and another where CA strengthens ML by leveraging linear CA evolution to process linear functions effectively. The book focuses on real-world applications of CA, such as in Computational Biology, where CAML models protein chains to predict mutations linked to human diseases, using carefully designed CA rule sequences for each amino acid. Another significant application is in multi-language Sentiment Analysis, where the model analyzes text in five languages (Hindi, Arabic, English, Greek, and Georgian), without relying on pre-trained language models. CAML uses CA rules for Unicode character modeling, offering a transparent, interpretable prediction algorithm. Overall, CAML aims to drive industrial and societal applications of CA, with an emphasis on transparent results and efficient hardware design through CA’s regular, modular, and scalable structure.