توضیحات
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.
This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated Programming Tips that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.
This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
From the Back Cover
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated Programming Tips that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
About the Author
Dr. Jos Unpingco completed his PhD at the University of California, San Diego in 1997 and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data processing and analysis topics, with deep experience in machine learning and statistics. As the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD), he spearheaded the DoD-wide adoption of scientific Python. He also trained over 600 scientists and engineers to effectively utilize Python for a wide range of scientific topics — from weather modeling to antenna analysis. Dr. Unpingco is the cofounder and Senior Director for Data Science at a non-profit Medical Research Organization in San Diego, California. He also teaches programming for data analysis at the University of California, San Diego for engineering undergraduate/graduate students. He is author of Python for Signal Processing (Springer 2014) and P ython for Probability, Statistics, and Machine Learning (2016)
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ترجمه ماشینی :
این کتاب که بهطور کامل برای پایتون نسخه 3.6+ بهروزرسانی شده است، ایدههای کلیدی را پوشش میدهد که احتمال، آمار و یادگیری ماشین را با استفاده از ماژولهای پایتون در این زمینهها به هم پیوند میدهند. تمام ارقام و نتایج عددی با استفاده از کدهای پایتون ارائه شده قابل تکرار هستند. نویسنده شهودهای کلیدی در یادگیری ماشین را با استفاده از مثالهای معنیدار با استفاده از روشهای تحلیلی متعدد و کدهای پایتون توسعه میدهد و در نتیجه مفاهیم نظری را به پیادهسازیهای عینی متصل میکند. شواهد دقیق برای برخی از نتایج مهم نیز ارائه شده است. ماژولهای پایتون مدرن مانند Pandas، Sympy، Scikit-learn، Tensorflow و Keras برای شبیهسازی و تجسم مفاهیم مهم یادگیری ماشین مانند مبادله بایاس/واریانس، اعتبارسنجی متقابل و منظمسازی استفاده میشوند. بسیاری از ایده های ریاضی انتزاعی، مانند همگرایی در نظریه احتمال، توسعه یافته و با مثال های عددی نشان داده شده اند.
این نسخه به روز شده اکنون شامل آزمون دقیق فیشر و آزمون من-ویتنی-ویلکاکسون است. بخش جدیدی در مورد تجزیه و تحلیل بقا و همچنین توسعه قابل توجهی از مدل های خطی تعمیم یافته گنجانده شده است. بخش جدید یادگیری عمیق برای پردازش تصویر شامل یک بحث عمیق در مورد روشهای نزول گرادیان است که زیربنای همه الگوریتمهای یادگیری عمیق است. مانند نسخه قبلی، نکات برنامه نویسی جدید و به روز شده ای وجود دارد که ماژول ها و روش های موثر پایتون را برای برنامه نویسی علمی و یادگیری ماشین نشان می دهد. 445 بلوک کد قابل اجرا با خروجی های مربوطه وجود دارد که از نظر دقت آزمایش شده اند. بیش از 158 تجسم گرافی
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