Matlab Machine Learning Recipes - A Problem-Solution Approach Format Beau livre

Matlab Machine Learning Recipes - A Problem-Solution Approach - Paluszek Michael

Format: Beau livre

Note : 0

Soyez le premier à donner un avis

Prix neuf 39,50 €

Qu'est-ce que le prix barré ?

C'est le prix de vente au public, fixé par l'éditeur ou l'importateur, pour le même article neuf.

11,34 €

Occasion · Très Bon État

Livraison GRATUITE
Livré entre le 21 et le 24 septembre

Voir les modes de livraison

PRO Vendeur favori

4,8/5 sur + de 1 000 ventes

Livré gratuitement chez vous en 2 semaines. Article presque inutilisé, absence presque totale de traces d'utilisation. 2 millions de ventes réalisées en 5 ans, merci de votre confiance ! Découvrez les avis...

Voir le détail de l'annonce

Vous avez choisi le retrait chez le vendeur à

Payez directement sur Rakuten (CB, PayPal, 4xCB...)
Récupérez le produit directement chez le vendeur
Rakuten vous rembourse en cas de problème

Terminer ma commande

Gratuit et sans engagement

Félicitations !

Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

TROUVER UN MAGASIN

Adresse

Retour

Horaires

Note :

Avis sur Matlab Machine Learning Recipes - A Problem - Solution Approach Format Beau livre - Livre Informatique

Note : 0 0 avis sur Matlab Machine Learning Recipes - A Problem - Solution Approach Format Beau livre - Livre Informatique

Donnez votre avis

Donnez votre avis sur ce produit

Votre note * : 1/5 2/5 3/5 4/5 5/5 Veuillez donner une note au produit

Le titre de votre avis* : Saisissez un titre

Quelques conseils :

Donnez le ton de votre avis, résumez-le en quelques mots qui apparaîtront comme le titre de votre avis.

Votre avis* : (40 caractères minimum) Saisissez un avis

Quelques conseils :

N'hésitez pas à mettre en avant les caractéristiques de ce produit, n'oubliez pas de mentionner ses avantages et/ou ses défauts éventuels. Décrivez précisément, argumentez, soyez convaincant !

Annuler

Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.

Produits similaires

Présentation Matlab Machine Learning Recipes - A Problem - Solution Approach Format Beau livre
- Livre Informatique

Livre Informatique - Paluszek Michael - 31/01/2019 - Beau livre

Auteur(s) : Paluszek Michael - Thomas Stéphanie

Editeur : Apress

Parution : 31/01/2019

Format : Moyen, de 350g à 1kg

Nombre de pages : 347

Expédition : 700

Dimensions : 25.8 x 17.7 x 2

ISBN : 9781484239155

Résumé :
Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes : A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. You will : Learn to write code for machine learning, adaptive control and estimation using MATLAB . See how these three areas complement each other . Understand why these three areas are needed for robust machine learning applications Use MATLAB graphics and visualization tools for machine learning . Code real-world examples in MATLAB for major applications of machine learning in big data.

Biographie:

Michael Paluszek is the co-author of MATLAB Recipes published by Apress. He is President of Princeton Satellite Systems, Inc. (PSS) in Plainsboro, New Jersey. Mr. Paluszek founded PSS in 1992 to provide aerospace consulting services. He used MATLAB to develop the control system and simulation for the Indostar-1 geosynschronous communications satellite, resulting in the launch of PSS' first commercial MATLAB toolbox, the Spacecraft Control Toolbox, in 1995. Since then he has developed toolboxes and software packages for aircraft, submarines, robotics, and fusion propulsion, resulting in PSS' current extensive product line. He is currently leading an Army research contract for precision attitude control of small satellites and working with the Princeton Plasma Physics Laboratory on a compact nuclear fusion reactor for energy generation and propulsion. Prior to founding PSS, Mr. Paluszek was an engineer at GE Astro Space in East Windsor, NJ. At GE he designed the Global Geospace Science Polar despun platform control system and led the design of the GPS IIR attitude control system, the Inmarsat-3 attitude control systems and the Mars Observer delta-V control system, leveraging MATLAB for control design. Mr. Paluszek also worked on the attitude determination system for the DMSP meteorological satellites. Mr. Paluszek flew communication satellites on over twelve satellite launches, including the GSTAR III recovery, the first transfer of a satellite to an operational orbit using electric thrusters. At Draper Laboratory Mr. Paluszek worked on the Space Shuttle, Space Station and submarine navigation. His Space Station work included designing of Control Moment Gyro based control systems for attitude control. Mr. Paluszek received his bachelors in Electrical Engineering, and master's and engineer's degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology. He is author of numerous papers and has over a dozen U.S. Patents.

Stephanie Thomas is the co-author of MATLAB Recipes, published by Apress. She received her bachelor's and master's degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 1999 and 2001. Ms. Thomas was introduced to PSS' Spacecraft Control Toolbox for MATLAB during a summer internship in 1996 and has been using MATLAB for aerospace analysis ever since. She built a simulation of a lunar transfer vehicle in C++, LunarPilot, during the same internship. In her nearly 20 years of MATLAB experience, she has developed many software tools including the Solar Sail Module for the Spacecraft Control Toolbox; a proximity satellite operations toolbox for the Air Force; collision monitoring Simulink blocks for the Prisma satellite mission; and launch vehicle analysis tools in MATLAB and Java, to name a few. She has developed novel methods for space situation assessment such as a numeric approach to assessing the general rendezvous problem between any two satellites implemented in both MATLAB and C++. Ms. Thomas has contributed to PSS' Attitude and Orbit Control textbook, featuring examples using the Spacecraft Control Toolbox, and written many software User's Guides. She has conducted SCT training for engineers from diverse locales such as Australia, Canada, Brazil, and Thailand and has performed MATLAB consulting for NASA, the Air Force, and the European Space Agency.

Sommaire:

1 Overview

2 Data Representation

3 MATLAB Graphics

4 Kalman Filters

5 Adaptive Control

6 Fuzzy Logic

7 Data Classification with Decision Trees

8 Simple Neural Nets

9 Classification with Neural Nets

10 Neural Nets with Deep Learning

11 Neural Aircraft Control

12 Multiple Hypothesis Testing

13 Autonomous Driving with MHT

14 Case-Based Expert Systems

Appendix A: A Brief History of Autonomous Learning

Appendix B: Software for Machine Learning

Détails de conformité du produit

Consulter les détails de conformité de ce produit (

Personne responsable dans l'UE

)

Matlab Machine Learning Recipes - A Problem-Solution Approach - Paluszek Michael

Avis sur Matlab Machine Learning Recipes - A Problem - Solution Approach Format Beau livre - Livre Informatique

Présentation Matlab Machine Learning Recipes - A Problem - Solution Approach Format Beau livre - Livre Informatique

Présentation Matlab Machine Learning Recipes - A Problem - Solution Approach Format Beau livre
- Livre Informatique