MATLAB for Machine Learning: unlock the power of deep learning for swift and enhanced results
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
January 2024
|
| Ausgabe: | Second edition |
| Online-Zugang: | DE-1050 DE-91 DE-706 Volltext |
| Beschreibung: | 1 Online-Ressource (xix, 353 Seiten) |
| ISBN: | 9781835089538 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV049537588 | ||
| 003 | DE-604 | ||
| 005 | 20241209 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 240207s2024 xx o|||| 00||| eng d | ||
| 020 | |a 9781835089538 |9 978-1-83508-953-8 | ||
| 035 | |a (OCoLC)1422448359 | ||
| 035 | |a (DE-599)BVBBV049537588 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1050 |a DE-706 |a DE-91 | ||
| 100 | 1 | |a Ciaburro, Giuseppe |e Verfasser |0 (DE-588)1158671741 |4 aut | |
| 245 | 1 | 0 | |a MATLAB for Machine Learning |b unlock the power of deep learning for swift and enhanced results |c Giuseppe Ciaburro |
| 250 | |a Second edition | ||
| 264 | 1 | |a Birmingham, UK |b Packt Publishing |c January 2024 | |
| 300 | |a 1 Online-Ressource (xix, 353 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-83508-769-5 |
| 856 | 4 | 0 | |u https://portal.igpublish.com/iglibrary/search/PACKT0007064.html |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-30-PQE | ||
| 912 | |a ZDB-221-PDA | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034883042 | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=31084792 |l DE-1050 |p ZDB-30-PQE |q FHD01_PQE_Kauf |x Aggregator |3 Volltext | |
| 966 | e | |u https://portal.igpublish.com/iglibrary/search/PACKT0007064.html |l DE-91 |p ZDB-221-PDA |q TUM_Paketkauf_2025 |x Verlag |3 Volltext | |
| 966 | e | |u https://portal.igpublish.com/iglibrary/search/PACKT0007064.html |l DE-706 |p ZDB-221-PDA |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1817968385580859392 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Ciaburro, Giuseppe |
| author_GND | (DE-588)1158671741 |
| author_facet | Ciaburro, Giuseppe |
| author_role | aut |
| author_sort | Ciaburro, Giuseppe |
| author_variant | g c gc |
| building | Verbundindex |
| bvnumber | BV049537588 |
| collection | ZDB-30-PQE ZDB-221-PDA |
| contents | Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Matlab -- Chapter 1: Exploring MATLAB for Machine Learning -- Technical requirements -- Introducing ML -- How to define ML -- Analysis of logical reasoning -- Learning strategy typologies -- Discovering the different types of learning processes -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Semi-supervised learning -- Transfer learning -- Using ML techniques -- Selecting the ML paradigm -- Step-by-step guide on how to build ML models Exploring MATLAB toolboxes for ML -- Statistics and Machine Learning Toolbox -- Deep Learning Toolbox -- Reinforcement Learning Toolbox -- Computer Vision Toolbox -- Text Analytics Toolbox -- ML applications in real life -- Summary -- Chapter 2: Working with Data in MATLAB -- Technical requirements -- Importing data into MATLAB -- Exploring the Import Tool -- Using the load() function to import files -- Reading ASCII-delimited files -- Exporting data from MATLAB -- Working with different types of data -- Working with images -- Audio data handling -- Exploring data wrangling Introducing data cleaning -- Discovering exploratory statistics -- EDA -- EDA in practice -- Introducing exploratory visualization -- Understanding advanced data preprocessing techniques in MATLAB -- Data normalization for feature scaling -- Introducing correlation analysis in MATLAB -- Summary -- Part 2: Understanding Machine Learning Algorithms in MATLAB -- Chapter 3: Prediction Using Classification and Regression -- Technical requirements -- Introducing classification methods using MATLAB -- Decision trees for decision-making -- Exploring decision trees in MATLAB Building an effective and accurate classifier -- SVMs explained -- Supervised classification using SVM -- Exploring different types of regression -- Introducing linear regression -- Linear regression model in MATLAB -- Making predictions with regression analysis in MATLAB -- Multiple linear regression with categorical predictor -- Evaluating model performance -- Reducing outlier effects -- Using advanced techniques for model evaluation and selection in MATLAB -- Understanding k-fold cross-validation -- Exploring leave-one-out cross-validation -- Introducing the bootstrap method -- Summary Chapter 4: Clustering Analysis and Dimensionality Reduction -- Technical requirements -- Understanding clustering -- basic concepts and methods -- How to measure similarity -- How to find centroids and centers -- How to define a grouping -- Understanding hierarchical clustering -- Partitioning-based clustering algorithms with MATLAB -- Introducing the k-means algorithm -- Using k-means in MATLAB -- Grouping data using the similarity measures -- Applying k-medoids in MATLAB -- Discovering dimensionality reduction techniques -- Introducing feature selection methods |
| ctrlnum | (OCoLC)1422448359 (DE-599)BVBBV049537588 |
| edition | Second edition |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV049537588</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20241209</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">240207s2024 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781835089538</subfield><subfield code="9">978-1-83508-953-8</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1422448359</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049537588</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1050</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-91</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ciaburro, Giuseppe</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1158671741</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">MATLAB for Machine Learning</subfield><subfield code="b">unlock the power of deep learning for swift and enhanced results</subfield><subfield code="c">Giuseppe Ciaburro</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing</subfield><subfield code="c">January 2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xix, 353 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-83508-769-5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://portal.igpublish.com/iglibrary/search/PACKT0007064.html</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-221-PDA</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034883042</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=31084792</subfield><subfield code="l">DE-1050</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">FHD01_PQE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://portal.igpublish.com/iglibrary/search/PACKT0007064.html</subfield><subfield code="l">DE-91</subfield><subfield code="p">ZDB-221-PDA</subfield><subfield code="q">TUM_Paketkauf_2025</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://portal.igpublish.com/iglibrary/search/PACKT0007064.html</subfield><subfield code="l">DE-706</subfield><subfield code="p">ZDB-221-PDA</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV049537588 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:27:59Z |
| indexdate | 2024-12-09T13:09:16Z |
| institution | BVB |
| isbn | 9781835089538 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034883042 |
| oclc_num | 1422448359 |
| open_access_boolean | |
| owner | DE-1050 DE-706 DE-91 DE-BY-TUM |
| owner_facet | DE-1050 DE-706 DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xix, 353 Seiten) |
| psigel | ZDB-30-PQE ZDB-221-PDA ZDB-30-PQE FHD01_PQE_Kauf ZDB-221-PDA TUM_Paketkauf_2025 |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing |
| record_format | marc |
| spelling | Ciaburro, Giuseppe Verfasser (DE-588)1158671741 aut MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results Giuseppe Ciaburro Second edition Birmingham, UK Packt Publishing January 2024 1 Online-Ressource (xix, 353 Seiten) txt rdacontent c rdamedia cr rdacarrier Erscheint auch als Druck-Ausgabe 978-1-83508-769-5 https://portal.igpublish.com/iglibrary/search/PACKT0007064.html Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Ciaburro, Giuseppe MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Matlab -- Chapter 1: Exploring MATLAB for Machine Learning -- Technical requirements -- Introducing ML -- How to define ML -- Analysis of logical reasoning -- Learning strategy typologies -- Discovering the different types of learning processes -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Semi-supervised learning -- Transfer learning -- Using ML techniques -- Selecting the ML paradigm -- Step-by-step guide on how to build ML models Exploring MATLAB toolboxes for ML -- Statistics and Machine Learning Toolbox -- Deep Learning Toolbox -- Reinforcement Learning Toolbox -- Computer Vision Toolbox -- Text Analytics Toolbox -- ML applications in real life -- Summary -- Chapter 2: Working with Data in MATLAB -- Technical requirements -- Importing data into MATLAB -- Exploring the Import Tool -- Using the load() function to import files -- Reading ASCII-delimited files -- Exporting data from MATLAB -- Working with different types of data -- Working with images -- Audio data handling -- Exploring data wrangling Introducing data cleaning -- Discovering exploratory statistics -- EDA -- EDA in practice -- Introducing exploratory visualization -- Understanding advanced data preprocessing techniques in MATLAB -- Data normalization for feature scaling -- Introducing correlation analysis in MATLAB -- Summary -- Part 2: Understanding Machine Learning Algorithms in MATLAB -- Chapter 3: Prediction Using Classification and Regression -- Technical requirements -- Introducing classification methods using MATLAB -- Decision trees for decision-making -- Exploring decision trees in MATLAB Building an effective and accurate classifier -- SVMs explained -- Supervised classification using SVM -- Exploring different types of regression -- Introducing linear regression -- Linear regression model in MATLAB -- Making predictions with regression analysis in MATLAB -- Multiple linear regression with categorical predictor -- Evaluating model performance -- Reducing outlier effects -- Using advanced techniques for model evaluation and selection in MATLAB -- Understanding k-fold cross-validation -- Exploring leave-one-out cross-validation -- Introducing the bootstrap method -- Summary Chapter 4: Clustering Analysis and Dimensionality Reduction -- Technical requirements -- Understanding clustering -- basic concepts and methods -- How to measure similarity -- How to find centroids and centers -- How to define a grouping -- Understanding hierarchical clustering -- Partitioning-based clustering algorithms with MATLAB -- Introducing the k-means algorithm -- Using k-means in MATLAB -- Grouping data using the similarity measures -- Applying k-medoids in MATLAB -- Discovering dimensionality reduction techniques -- Introducing feature selection methods |
| title | MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results |
| title_auth | MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results |
| title_exact_search | MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results |
| title_exact_search_txtP | MATLAB for Machine Learning Unlock the Power of Deep Learning for Swift and Enhanced Results |
| title_full | MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results Giuseppe Ciaburro |
| title_fullStr | MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results Giuseppe Ciaburro |
| title_full_unstemmed | MATLAB for Machine Learning unlock the power of deep learning for swift and enhanced results Giuseppe Ciaburro |
| title_short | MATLAB for Machine Learning |
| title_sort | matlab for machine learning unlock the power of deep learning for swift and enhanced results |
| title_sub | unlock the power of deep learning for swift and enhanced results |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0007064.html |
| work_keys_str_mv | AT ciaburrogiuseppe matlabformachinelearningunlockthepowerofdeeplearningforswiftandenhancedresults |