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Hands-on machine learning with Pythonimplement neural network solutions with Scikit-learn and PyTorch /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hands-on machine learning with Pythonby Ashwin Pajankar, Aditya Joshi.
Reminder of title:	implement neural network solutions with Scikit-learn and PyTorch /
Author:	Pajankar, Ashwin.
other author:	Joshi, Aditya.
Published:	Berkeley, CA :Apress :2022.
Description:	xx, 335 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-7921-2
ISBN:	9781484279212$q(electronic bk.)

Hands-on machine learning with Pythonimplement neural network solutions with Scikit-learn and PyTorch /
Pajankar, Ashwin.

Hands-on machine learning with Pythonimplement neural network solutions with Scikit-learn and PyTorch /[electronic resource] :by Ashwin Pajankar, Aditya Joshi. - Berkeley, CA :Apress :2022. - xx, 335 p. :ill., digital ;24 cm.

Chapter 1: Getting Started with Python 3 and Jupyter Notebook -- Chapter 2: Getting Started with NumPy -- Chapter 3 : Introduction to Data Visualization -- Chapter 4 : Introduction to Pandas -- Chapter 5: Introduction to Machine Learning with Scikit-Learn -- Chapter 6: Preparing Data for Machine Learning -- Chapter 7: Supervised Learning Methods - 1 -- Chapter 8: Tuning Supervised Learners -- Chapter 9: Supervised Learning Methods - 2 -- Chapter 10: Ensemble Learning Methods -- Chapter 11: Unsupervised Learning Methods -- Chapter 12: Neural Networks and Pytorch Basics -- Chapter 13: Feedforward Neural Networks -- Chapter 14: Convolutional Neural Network -- Chapter 15: Recurrent Neural Network -- Chapter 16: Bringing It All Together.

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.

ISBN: 9781484279212$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-7921-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .P35 2022

Dewey Class. No.: 006.31

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505 0 $a Chapter 1: Getting Started with Python 3 and Jupyter Notebook -- Chapter 2: Getting Started with NumPy -- Chapter 3 : Introduction to Data Visualization -- Chapter 4 : Introduction to Pandas -- Chapter 5: Introduction to Machine Learning with Scikit-Learn -- Chapter 6: Preparing Data for Machine Learning -- Chapter 7: Supervised Learning Methods - 1 -- Chapter 8: Tuning Supervised Learners -- Chapter 9: Supervised Learning Methods - 2 -- Chapter 10: Ensemble Learning Methods -- Chapter 11: Unsupervised Learning Methods -- Chapter 12: Neural Networks and Pytorch Basics -- Chapter 13: Feedforward Neural Networks -- Chapter 14: Convolutional Neural Network -- Chapter 15: Recurrent Neural Network -- Chapter 16: Bringing It All Together.
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.
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