Random thoughts about random subjects… From science to literature and between manga and watercolours, passing by data science and rugby; including film, physics and fiction, programming, pictures and puns.
I had read about the story when the news was breaking, and it was interesting then. The book has been in my reading pile for a while and with my recent watching “The inventor: Out for blood in Silicon Valley” I had to bump it up the list. Here we go!
Now reading: Hearing, an introduction & practical guide. Edited by JR Tysome and RG Kanegaonkar
Hearing Hearing is essential for normal communication. We are able to localise sound with surprising accuracy and can detect time differences as small as the time it takes for sound to pass from the mouth of one person to the ear of another. However, hearing loss is underdiagnosed, poorly understood and a common cause of social isolation.Hearing: An Introduction and Practical Guideprovides a basic understanding of the science of hearing, the causes of hearing loss and how hearing loss can be clinically assessed and effectively treated.
The book is divided into three sections, beginning with a review of the basic anatomy, physiology and principles of hearing. The second section addresses clinical and audiological assessment of hearing as well as imaging of the ear. The third section features an extensive series of chapters on focused topics covering the range of causes of hearing loss, their management and options for hearing rehabilitation.
Clear, concise and comprehensive,Hearing: An Introduction and Practical Guideis an excellent source of information for ENT surgeons, general practitioners and trainees. It presents a quick reference and practical guide for assessing and managing patients with hearing loss.
There you go, the first checkpoint is completed: I have officially submitted the completed version of “Advanced Data Science and Analytics with Python”.
The book has been some time in the making (and in the thinking…). It is a follow up from my previous book, imaginatively called “Data Science and Analytics with Python” . The book covers aspects that were necessarily left out in the previous volume; however, the readers in mind are still technical people interested in moving into the data science and analytics world. I have tried to keep the same tone as in the first book, peppering the pages with some bits and bobs of popular culture, science fiction and indeed Monty Python puns.
Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow up from the topics discuss in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The development is also supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.
The book can be read independently form the previous volume and each of the chapters in this volume is sufficiently independent from the others proving flexibiity for the reader. Each of the topics adressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book
Time series analysis, natural language processing, topic modelling, social network analysis, neural networds and deep learning are comprehensively covrered in the book. The book discusses the need to develop data products and tackles the subject of bringing models to their intended audiences. In this case literally to the users fingertips in the form of an iPhone app.
While the book is still in the oven, you may want to take a look at the first volume. You can get your copy here: