Cyrille Rossant – författare

Since the early 2000s, a growing body of scientific studies in neuropathology, neurology, neurosurgery, biomechanics, statistics, criminology and psychology has cast doubt on the forensic reliability of medical determinations of Shaken Baby Syndrome (SBS), more recently termed Abusive Head Trauma (AHT). Studies have increasingly documented that accidental short falls and a wide range of medical conditions, can cause the same symptoms and findings associated with this syndrome. Nevertheless, inaccurate diagnoses, unrealistic confidence expression, and wrongful convictions continue to this day. Bringing together contributions from a multidisciplinary expert panel of 32 professionals across 8 countries in 16 different specialties, this landmark book tackles the highly controversial topic of SBS, which lies at the intersection of medicine, science, and law. With comprehensive coverage across multiple disciplines, it explains the scientific evidence challenging SBS and advances efforts to evaluate how deaths and serious brain injuries in infants should be analysed and investigated.

Key FeaturesBook DescriptionIntended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.What you will learnCode better by writing highquality, readable, and welltested programs; profiling and optimizing your code, and conducting reproducible interactive computing experimentsMaster all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgetsAnalyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikitlearn)Gain valuable insights into signals, images, and sounds with SciPy, scikitimage, and OpenCVLearn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many moreWho this book is for

Get started with Python for data analysis and numerical computing in the Jupyter NotebookKey FeaturesLearn the basics of Python in the Jupyter NotebookAnalyze and visualize data with pandas, NumPy, matplotlib, and seabornPerform highly-efficient numerical computations with Numba, Cython, and ipyparallelBook DescriptionPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.What you will learnInstall Anaconda and code in Python in the Jupyter NotebookLoad and explore datasets interactivelyPerform complex data manipulations effectively with pandasCreate engaging data visualizations with matplotlib and seabornSimulate mathematical models with NumPyVisualize and process images interactively in the Jupyter Notebook with scikit-imageAccelerate your code with Numba, Cython, and IPython.parallelExtend the Notebook interface with HTML, JavaScript, and D3Who this book is forThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.