Hands-on machine learning with Python: implement neural network solutions with Scikit-learn and PyTorch
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytor...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[Berkeley]
Apress
[2022]
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484279212/?ar |
| Summary: | Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. You will: Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory . |
| Item Description: | Includes index. - Includes bibliographical references and index. - Print version record |
| Physical Description: | 1 Online-Ressource illustrations |
| ISBN: | 9781484279212 1484279212 |
Staff View
MARC
| LEADER | 00000cam a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-077854713 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121615.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220513s2022 xx |||||o 00| ||eng c | ||
| 020 | |a 9781484279212 |c electronic bk. |9 978-1-4842-7921-2 | ||
| 020 | |a 1484279212 |c electronic bk. |9 1-4842-7921-2 | ||
| 035 | |a (DE-627-1)077854713 | ||
| 035 | |a (DE-599)KEP077854713 | ||
| 035 | |a (ORHE)9781484279212 | ||
| 035 | |a (DE-627-1)077854713 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 072 | 7 | |a COM004000 |2 bisacsh | |
| 082 | 0 | |a 006.3/1 |2 23 | |
| 100 | 1 | |a Pajankar, Ashwin |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Hands-on machine learning with Python |b implement neural network solutions with Scikit-learn and PyTorch |c Ashwin Pajankar, Aditya Joshi |
| 264 | 1 | |a [Berkeley] |b Apress |c [2022] | |
| 264 | 4 | |c ©2022 | |
| 300 | |a 1 Online-Ressource |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. - Includes bibliographical references and index. - Print version record | ||
| 520 | |a Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. You will: Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory . | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Python (Computer program language) | |
| 700 | 1 | |a Joshi, Aditya |e VerfasserIn |4 aut | |
| 776 | 1 | |z 1484279204 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 1484279204 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781484279212/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-30-ORH-077854713 |
|---|---|
| _version_ | 1829007735781326848 |
| adam_text | |
| any_adam_object | |
| author | Pajankar, Ashwin Joshi, Aditya |
| author_facet | Pajankar, Ashwin Joshi, Aditya |
| author_role | aut aut |
| author_sort | Pajankar, Ashwin |
| author_variant | a p ap a j aj |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)077854713 (DE-599)KEP077854713 (ORHE)9781484279212 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03489cam a22004812c 4500</leader><controlfield tag="001">ZDB-30-ORH-077854713</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121615.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220513s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781484279212</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4842-7921-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1484279212</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4842-7921-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)077854713</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP077854713</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781484279212</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)077854713</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM004000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Pajankar, Ashwin</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hands-on machine learning with Python</subfield><subfield code="b">implement neural network solutions with Scikit-learn and PyTorch</subfield><subfield code="c">Ashwin Pajankar, Aditya Joshi</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Berkeley]</subfield><subfield code="b">Apress</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index. - Includes bibliographical references and index. - Print version record</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. You will: Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory .</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Joshi, Aditya</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">1484279204</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">1484279204</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781484279212/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-077854713 |
| illustrated | Illustrated |
| indexdate | 2025-04-10T09:35:02Z |
| institution | BVB |
| isbn | 9781484279212 1484279212 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Apress |
| record_format | marc |
| spelling | Pajankar, Ashwin VerfasserIn aut Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch Ashwin Pajankar, Aditya Joshi [Berkeley] Apress [2022] ©2022 1 Online-Ressource illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Includes bibliographical references and index. - Print version record Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. You will: Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory . Machine learning Python (Computer program language) Apprentissage automatique Python (Langage de programmation) Joshi, Aditya VerfasserIn aut 1484279204 Erscheint auch als Druck-Ausgabe 1484279204 |
| spellingShingle | Pajankar, Ashwin Joshi, Aditya Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch Machine learning Python (Computer program language) Apprentissage automatique Python (Langage de programmation) |
| title | Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch |
| title_auth | Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch |
| title_exact_search | Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch |
| title_full | Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch Ashwin Pajankar, Aditya Joshi |
| title_fullStr | Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch Ashwin Pajankar, Aditya Joshi |
| title_full_unstemmed | Hands-on machine learning with Python implement neural network solutions with Scikit-learn and PyTorch Ashwin Pajankar, Aditya Joshi |
| title_short | Hands-on machine learning with Python |
| title_sort | hands on machine learning with python implement neural network solutions with scikit learn and pytorch |
| title_sub | implement neural network solutions with Scikit-learn and PyTorch |
| topic | Machine learning Python (Computer program language) Apprentissage automatique Python (Langage de programmation) |
| topic_facet | Machine learning Python (Computer program language) Apprentissage automatique Python (Langage de programmation) |
| work_keys_str_mv | AT pajankarashwin handsonmachinelearningwithpythonimplementneuralnetworksolutionswithscikitlearnandpytorch AT joshiaditya handsonmachinelearningwithpythonimplementneuralnetworksolutionswithscikitlearnandpytorch |