Analysis And Visualization Of Discrete Data Using Neural Networks
Regular price
€92.99
Regular price
€93.99
Sale
Sale price
€92.99
14 days return policy
Shipping & Delivery
A01=Koji Koyamada
Age Group_Uncategorized
Age Group_Uncategorized
Author_Koji Koyamada
Automatic Differentiation in NN
automatic-update
CAE Surrogate Model
Carbon Neutrality (CN) Application
Category1=Non-Fiction
Category=UYQN
COP=Singapore
Data Analysis Using NNs
Deep Learning with Colab
Delivery_Delivery within 10-20 working days
Derivation of PDEs
eq_bestseller
eq_computing
eq_isMigrated=0
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Excel Operations
Excel Solver
Fourier Transform
Language_English
Methods for Solving PDEs
NNs (Neural Networks)
Optimization
PA=Available
PDE Approximate Solution
PDE Derivation using Regression Analysis
PDE Derivation with Excel
Physically Based Surrogate Model
PINNs (Physics-Informed Neural Networks)
Point Cloud Data Analysis using NNs
Price_€50 to €100
PS=Active
Regression Analysis using NNs
softlaunch
Statistical Analysis in Excel
Universal Approximation Theorem
Visual Analysis Techniques
Visualization with Colab
Visualization with Excel
What-if Analysis
Product details
- ISBN 9789811283611
- Publication Date: 16 Feb 2024
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
- Language: English
Secure
checkout
Fast Shipping
Easy returns
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.
