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Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition Luis Pedro Coelho, Willi Richert, Matthieu Brucher

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Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition Luis Pedro Coelho, Willi Richert, Matthieu Brucher - okladka książki

Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition Luis Pedro Coelho, Willi Richert, Matthieu Brucher - okladka książki

Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition Luis Pedro Coelho, Willi Richert, Matthieu Brucher - audiobook MP3

Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition Luis Pedro Coelho, Willi Richert, Matthieu Brucher - audiobook CD

Autorzy:
Luis Pedro Coelho, Willi Richert, Matthieu Brucher
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
406
Dostępne formaty:
     PDF
     ePub
     Mobi
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Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI).
With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance.
By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.

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O autorach książki

Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.
Willi Richert is a Software Development Engineer at Microsoft for the last 5 years. He has also worked as a project team leader for mobile devices (Java/Python) at Richert GbR.
Matthieu Brucher is a computer scientist who specializes in high-performance computing and computational modeling and currently works for JPMorgan in their quantitative research branch. He is also the lead developer of Audio ToolKit, a library for real-time audio signal processing. He has a PhD in machine learning and signals processing from the University of Strasbourg, two Master of Science degreesone in digital electronics and signal processing and another in automation from the University of Paris XI and Supelec, as well as a Master of Music degree from Bath Spa University.

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