Introduction To Neural Networks Javatpoint
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Artificial Intelligence and Knowledge Processing
Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.
Introduction And Fundamental Concepts Of Machine Learning
Author: Dr. P. Sumithabhashini
language: en
Publisher: Academic Guru Publishing House
Release Date: 2022-12-13
The field of machine learning is gaining a lot of attention around the world, both in the research community and in the business world. Learning by machine is becoming increasingly important in many aspects of modern life. Deep learning neural networks have been responsible for several recent technological advances, including those in the fields of computer vision, voice processing, machine translation, and reinforcement learning. As a direct consequence of this, neural networks have developed into an indispensable instrument in the toolset of every data scientist. This book explains neural networks, including what they are, why they are effective algorithms and why they have the structure that they do. It starts by discussing the fundamental elements that make up a neural network (i.e., nodes, weights, activation functions, biases, and layers). This book is meant to serve as an introduction to machine learning, which is a field that is quickly becoming more significant in today's technological landscape. Utilize to the fullest the vast potential that is afforded by various methodologies such as predictive analysis, classifiers, clustering, and Natural Language Processing (NLP). Since mathematical expertise is required not only for describing the algorithms but also for demonstrating to the reader how and where to set the hyperparameters for maximum accuracy, it is essential to have at least a surface-level understanding of the subject matter. This is because mathematical expertise is required. It should not come as a surprise that there are far too many elements to this issue for all of them to be covered. There are far too many facets to be included.
Introduction to Machine Learning and Neural Classification
Introduction to Machine Learning and Neural Classification is your gateway to understanding the fundamental aspects of machine learning, a subset of AI, as well as neural networks and statistical classification. As machine learning becomes increasingly integral to our lives, this book covers every significant topic with clarity and precision. We start with an introduction to key terms like Data Science, Machine Learning, Data Mining, Neural Networks, and Statistical Classification. We then explore classical and modern statistical techniques and methods. The book dives into decision tree rules in machine learning and covers neural networks, including methods of comparison and empirical analysis. Readers will also learn about descriptive statistics, knowledge representation, control dynamic systems, and data mining algorithms. Throughout the book, explanatory diagrams, bar graphs, and tables are provided to enhance understanding. Whether you're a beginner or looking to deepen your knowledge, this book provides comprehensive insights into these fascinating topics, making it an essential read for anyone interested in machine learning and AI.