Luis Pedro Coelho - Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition

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First published: July 2013

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Third edition: July 2018

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Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informaticsthe 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 has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++only to finally arrive at his true love: Python.

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.

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from NLP, behavioral analysis, and machine learning to deep nets and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

Gerold Hintz is an applied scientist who specializes in NLP. He obtained an MSc in Computer Science from Darmstadt University of Technology in 2014, focusing on machine learning and minoring in linguistics. He worked as a researcher in the field of computational semantics, applying unsupervised methods to paraphrasing tasks. He likes to study both natural languages and programming languages, and has a particular interest in the intersection of these fields.

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Machine learning allows models or systems to learn without being explicitly programmed. You will see how to use the best library support available, including, scikit-learn, TensorFlow, and many others, to build efficient, smart systems.

Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.