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Titlebook: Hands-on Machine Learning with Python; Implement Neural Net Ashwin Pajankar,Aditya Joshi Book 2022 Ashwin Pajankar and Aditya Joshi 2022 Ma

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發(fā)表于 2025-3-21 17:41:11 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Hands-on Machine Learning with Python
副標(biāo)題Implement Neural Net
編輯Ashwin Pajankar,Aditya Joshi
視頻videohttp://file.papertrans.cn/424/423990/423990.mp4
概述Explains machine learning process through validation, evaluation, hyperparameter tuning and regularization.Discusses neural network architectures for predicting sequences in the form of Recurrent Neur
圖書封面Titlebook: Hands-on Machine Learning with Python; Implement Neural Net Ashwin Pajankar,Aditya Joshi Book 2022 Ashwin Pajankar and Aditya Joshi 2022 Ma
描述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 theoreticaland 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-en
出版日期Book 2022
關(guān)鍵詞Machine Learning; Python; Data Science; Numpy; Pandas; Matplotlib; CNN; RNN; LSTM; Keras; TensorFlow; PyTorch
版次1
doihttps://doi.org/10.1007/978-1-4842-7921-2
isbn_softcover978-1-4842-7920-5
isbn_ebook978-1-4842-7921-2
copyrightAshwin Pajankar and Aditya Joshi 2022
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Ashwin Pajankar,Aditya Joshin measurements possible, including both optical and acoustic particle tracking, are reviewed. Then some of the laboratory flows used in Lagrangian measurements are described and a selection of new experimental results are presented.
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Getting Started with NumPye will have a lot of hands-on programming in this chapter. While the programming is not very difficult when it comes to NumPy and Python, the concepts are worth learning. I recommend all readers to spend some time to comprehend the ideas presented in this chapter.
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Introduction to Pandasthe routines to create, store, and visualize data with Python programming. In this chapter, we will be acquainted with the data science library of the Scientific Python Ecosystem, Pandas. We will learn the basic data structures, a few operations, and the recipes of visualization with Matplotlib.
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Neural Network and PyTorch Basicsemic research and industry applications for decades. However, the subject of focus in the new innovations in the past few years has been neural networks – the capability, the performance, and the versatility of various deep neural network architectures.
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