Venkateswaran / Ciaburro Neural Networks with R

Build smart systems by implementing popular deep learning models in R
1. Auflage 2017
ISBN: 978-1-78839-941-8
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 270 Seiten

No detailed description available for "Neural Networks with R".

Autoren/Hrsg.

Venkateswaran, Balaji

Ciaburro, Giuseppe

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Inhaltsverzeichnis

Table of Contents - Neural Network and Artificial Intelligence concepts

- Learning process in Neural Networks
- Deep Learning using multilayer neural networks
- Building Perceptron Neural Network modeling: basic models

- Training and visualizing a neural network in R
- Recurrent and Convolutional Neural networks
- Use cases of neural networks: advanced topics

Über Autor(innen)

Venkateswaran Balaji:

Balaji Venkateswaran is an AI expert, data scientist, machine learning practitioner, and database architect. He has 17+ years of experience in investment banking payment processing, telecom billing, and project management. He has worked for major companies such as ADP, Goldman Sachs, MasterCard, and Wipro. Balaji is a trainer in data science, Hadoop, and Tableau. He holds a postgraduate degree PG in business analytics from Great Lakes Institute of Management, Chennai. Balaji has expertise relating to statistics, classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures. His main interests are neural networks and deep learning. Balaji holds various certifications in IBM SPSS, IBM Watson, IBM big data architect, cloud architect, CEH, Splunk, Salesforce, Agile CSM, and AWS. If you have any questions, don't hesitate to message him on LinkedIn (balvenkateswaran); he will be more than glad to help fellow data scientists.Ciaburro Giuseppe:

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).