finelybook
Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch
Author: Ashwin Pajankar and Aditya Joshi
Publisher Finelybook 出版社: Apress; 1st ed. edition (March 6, 2022)
Language 语言: English
pages 页数: 356 pages
ISBN-10 书号: 1484279204
ISBN-13 书号: 9781484279205
Book Description
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.
What You’ll Learn
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
