Machine learning with pytorch and scikit-learn: develop machine learning and deep...
Описание
This book of the bestselling and widely acclaimed Python Machine Learning
series is a comprehensive guide to machine and deep learning using PyTorch's
simple to code framework. \nKey Features \n
\n
- Learn applied machine learning with a solid foundation in theory
\n
- Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
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- Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
\n
\nBook Description \nMachine Learning with PyTorch and Scikit-Learn is a
comprehensive guide to machine learning and deep learning with PyTorch. It
acts as both a step-by-step tutorial and a reference you'll keep coming back
to as you build your machine learning systems. \n \nPacked with clear
explanations, visualizations, and examples, the book covers all the essential
machine learning techniques in depth. While some books teach you only to
follow instructions, with this machine learning book, we teach the principles
allowing you to build models and applications for yourself. \n \nWhy PyTorch?
\n \nPyTorch is the Pythonic way to learn machine learning, making it easier
to learn and simpler to code with. This book explains the essential parts of
PyTorch and how to create models using popular libraries, such as PyTorch
Lightning and PyTorch Geometric. \n \nYou will also learn about generative
adversarial networks (GANs) for generating new data and training intelligent
agents with reinforcement learning. Finally, this new edition is expanded to
cover the latest trends in deep learning, including graph neural networks and
large-scale transformers used for natural language processing (NLP). \n \nThis
PyTorch book is your companion to machine learning with Python, whether you're
a Python developer new to machine learning or want to deepen your knowledge of
the latest developments. \nWhat you will learn \n \n
- Explore frameworks, models, and techniques for machines to 'learn' from data
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- Use scikit-learn for machine learning and PyTorch for deep learning
\n
- Train machine learning classifiers on images, text, and more
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- Build and train neural networks, transformers, and boosting algorithms
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- Discover best practices for evaluating and tuning models
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- Predict continuous target outcomes using regression analysis
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- Dig deeper into textual and social media data using sentiment analysis
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\nWho this book is for \nIf you have a good grasp of Python basics and want to
start learning about machine learning and deep learning, then this is the book
for you. This is an essential resource written for developers and data
scientists who want to create practical machine learning and deep learning
applications using scikit-learn and PyTorch. \n \nBefore you get started with
this book, you'll need a good understanding of calculus, as well as linear
algebra.
Також купити книгу Machine Learning with PyTorch and Scikit-Learn: Develop
machine learning and deep learning models with Python, Sebastian Raschka, Yuxi
(Hayden) Liu, Vahid Mirjalili можливо по посиланню:
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