[PDF] ↠ Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition 2, Sebastian Raschka, Vahid Mirjalili, eBook - Amazon.com Auteur Sebastian Raschka – Writerscompany.co.uk

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition 2, Sebastian Raschka, Vahid Mirjalili, eBook - Amazon.com I Bought The First Version Of This Book, And Now Also The Second The New Version Is Very Comprehensive If You Are Using Python It S Almost A Reference I Also Like The Emphasis On Neural Networks And TensorFlow Which In My View Is Where The Python Community Is HeadingI Am Also Planning To Use This Book In My Teaching At Oxford University The Data Pre Processing Sections Are Also Good I Found The Sequence Flow Slightly Unusual But For An Expert Level Audience, It S Not A Major IssueAjit Jaokar Data Science For IoT Course Creator And Lead Tutor At The University Of Oxford Principal Data ScientistSebastian Raschka, Author Of The Bestselling Book, Python Machine Learning, Has Many Years Of Experience With Coding In Python, And He Has Given Several Seminars On The Practical Applications Of Data Science, Machine Learning, And Deep Learning, Including A Machine Learning Tutorial At SciPy The Leading Conference For Scientific Computing In PythonWhile Sebastian S Academic Research Projects Are Mainly Centered Around Problem Solving In Computational Biology, He Loves To Write And Talk About Data Science, Machine Learning, And Python In General, And He Is Motivated To Help People Develop Data Driven Solutions Without Necessarily Requiring A Machine Learning BackgroundHis Work And Contributions Have Recently Been Recognized By The Departmental Outstanding Graduate Student Award , As Well As The ACM Computing Reviews Best Of Award In His Free Time, Sebastian Loves To Contribute To Open Source Projects, And The Methods That He Has Implemented Are Now Successfully Used In Machine Learning Competitions, Such As KaggleVahid Mirjalili Obtained His PhD In Mechanical Engineering Working On Novel Methods For Large Scale, Computational Simulations Of Molecular Structures Currently, He Is Focusing His Research Efforts On Applications Of Machine Learning In Various Computer Vision Projects At The Department Of Computer Science And Engineering At Michigan State UniversityVahid Picked Python As His Number One Choice Of Programming Language, And Throughout His Academic And Research Career He Has Gained Tremendous Experience With Coding In Python He Taught Python Programming To The Engineering Class At Michigan State University, Which Gave Him A Chance To Help Students Understand Different Data Structures And Develop Efficient Code In PythonWhile Vahid S Broad Research Interests Focus On Deep Learning And Computer Vision Applications, He Is Especially Interested In Leveraging Deep Learning Techniques To Extend Privacy In Biometric Data Such As Face Images So That Information Is Not Revealed Beyond What Users Intend To Reveal Further, He Also Collaborates With A Team Of Engineers Working On Self Driving Cars, Where He Designs Neural Network Models For The Fusion Of Multispectral Images For Pedestrian Detection

About the Author: Sebastian Raschka

Sebastian Raschka obtained his Ph.D in Biochemistry and Molecular Biology and Quantitative Biology from Michigan State University developing novel computational methods in the field of computational biology Among others, his research activities include the development of new deep learning architectures to solve problems in the field of biometrics.Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning over the years, including a machine learning tutorial at SciPy, the leading conference for scientific computing in Python Sebastian loves to write and talk about data science, machine learning, and Python, and he is very motivated to help people developing data driven solutions without necessarily requiring a machine learning background His work and contributions has been recently recognized by the ACM Best of Computing 2016 award and the departmental outstanding graduate student award 2016 2017.

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