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Hands-on Machine Learning with Python - Implement Neural Network Solutions with Scikit-learn and PyTorch (Paperback, 1st ed.)
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Hands-on Machine Learning with Python - Implement Neural Network Solutions with Scikit-learn and PyTorch (Paperback, 1st ed.)
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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 Who This Book Is For Data scientists, machine
learning engineers, and software professionals with basic skills in
Python programming.
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