深入学习Python机器学习:TensorFlow与Keras深度实践

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"Python_Machine_Learning(Book4You).pdf" 《Python机器学习》是Sebastian Raschka撰写的一本畅销书籍,该书第二版全面更新了内容,以适应不断发展的机器学习和深度学习领域。书中介绍了最新的Python开源库,包括TensorFlow深度学习库、Keras神经网络库以及scikit-learn机器学习库的最新更新。这本书旨在帮助初学者和已阅读第一版的读者理解并掌握机器学习和深度学习算法,同时引入了更多现代理念。 在机器学习领域,Python因其简洁易用和强大的科学计算库而成为首选语言。本书详细讲解了如何使用Python进行预测性分析,解锁更深层次的洞察力。作者Sebastian Raschka从零基础开始,逐步引导读者了解和应用机器学习和深度学习算法。 TensorFlow是一个广泛使用的深度学习框架,它允许开发者构建和训练复杂的神经网络模型。书中将介绍如何使用TensorFlow构建和优化模型,处理图像识别、自然语言处理等任务。 Keras是基于TensorFlow的高级神经网络API,简化了深度学习模型的构建过程。通过Keras,初学者可以快速上手,实现深度学习模型的搭建和训练。 scikit-learn是Python中最受欢迎的机器学习库,提供了各种经典的机器学习算法,如线性回归、决策树、支持向量机等。书中会涵盖如何使用scikit-learn进行数据预处理、模型选择和评估。 此外,书中还探讨了如何平衡经典机器学习方法与现代思想,让读者既能理解传统的算法,又能跟上深度学习的最新趋势。无论是对机器学习感兴趣的初学者,还是希望更新知识的从业者,都能从这本书中获益。 《Python机器学习》第二版是一本全面、深入的指南,涵盖了从基础的机器学习概念到前沿的深度学习技术,是学习和提升机器学习技能的理想教材。通过阅读本书,读者将能够运用Python和相关库开发出自己的机器学习和深度学习应用程序,以解决实际问题。
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原pdf书签没有链接正确,本人对此进行了修正 Paperback: 454 pages Publisher: Packt Publishing - ebooks Account (September 2015) Language: English ISBN-10: 1783555130 ISBN-13: 978-1783555130 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask and answer tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data