Deep Learning Models And Its Application An Overview With The Help Of R Software Second In Series Machine Learning
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Deep Learning Models and its application: An overview with the help of R software: Second in series (Machine Learning)
Author: Editor IJSMI
language: en
Publisher: International Journal of Statistics and Medical Informatics
Release Date: 2019-02-09
Deep Learning Models and its application: An overview with the help of R softwarePrefaceDeep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. This book intends to provide an overview of Deep Learning models, its application in the areas of image recognition & classification, sentiment analysis, natural language processing, stock market prediction using R statistical software package, an open source software package. The book also includes an introduction to python software package which is also open source software for the benefit of the users.This books is a second book in series after the author’s first book- Machine Learning: An Overview with the Help of R Software https://www.amazon.com/dp/B07KQSN447EditorInternational Journal of Statistics and Medical Informaticswww.ijsmi.com/book.php
Deep Learning Models explored with help of Python Programming
Author: Editor IJSMI
language: en
Publisher: International Journal of Statistics and Medical Informatics
Release Date: 2020-11-04
This is the second book in the Deep Learning models series by the author. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. The book starts with the Introduction part which is adopted from Author’s Deep Learning Models and its application: An overview with the help of R software book and move on to the Python’s important data processing packages such Numpy, and Pandas. Book then explores the Deep Learning models with the help of packages such as Pytorch, Tensor Flow and Keras and their applications in image processing, stock market prediction, recommender systems and natural language processing. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php ISBN: 9798558877953 E-Books: https://www.amazon.com/dp/B08MQTM1ZP Paperbacks: https://www.amazon.com/dp/B08MSQ3R8R
Human Machine Interaction in the Digital Era
The Human Machine Interaction in the Digital Era (ICHMIDE) 2023 conference aims to address the main issues of concern in the design issues with a particular emphasis on the design and development of interfaces for autonomous robots. Its main objective is to provide an international forum for the dissemination and exchange of up-to-date scientific information on research related to integrated human/machine systems at multiple scales, and includes areas such as human/machine interaction, engineering mathematical models, assistive technologies, system modelling, design, testing and validation. The organization of ICHMS is based on the following Track types: Smart Applications for Digital Era, Computational Mathematical and Electronics, Intelligent Systems in Security and Communication Technologies, Technological Interventions using AI and Machine Learning, Applied Science, and IoT Techniques for Industries.