The Hugo Awards, widely considered the most prestigious science fiction and fantasy prizes, were announced Friday, with female authors dominating and N.K. Women won both editing awards, with Ellen Datlow taking home the prize in the short form category and Liz Gorinsky winning the long form category. The Hugo Awards also honor television and movies, and this year, the film "Arrival" won for dramatic presentation, long form, beating "Ghostbusters," "Deadpool" and the first season of the television show "Stranger Things." The dramatic presentation, short form, award went to "Leviathan Wakes," an episode of the television series "The Expanse."
I heard about The Mathematical Corporation: Where Machine Intelligence and Human ... by Josh Sullivan and Angela Zutavern through a tweet by Kirk Bourne. In essence, Mathematical Corporation calls for new leadership traits in the world of AI. Leaders embrace complexity ("Complexity is the new treasure", "Complexity is a boon not a burden") Leaders will prefer complex models which solve Big problems over over-simplified models which could lose the essence. The book link is The Mathematical Corporation: Where Machine Intelligence and Human ... by Josh Sullivan and Angela Zutavern
Top Machine Learning & Data Mining Books - in this post, we have scraped various signals (e.g. A highly rated book on Amazon written by a well-known author Christopher M. Bishop who is a distinguished Scientist at Microsoft Research in Cambridge where he leads the Machine Learning and Perception group. The "Machine Learning" is a well-know book in the field of Machine Learning written by Tom Mitchell - an American computer scientist professor from the Carnegie Mellon University. This foundational text is a comprehensive introduction to statistical natural language processing (NLP).
It requires skills and techniques which can be honed by using books like data mining: practical machine learning tools and techniques. Data mining: practical machine learning tools and techniques in the new edition includes all the recent changes and modernisation of techniques of data mining. Data mining: practical machine learning tools and techniques is now available in its third edition in paperback by elsevier. Key features: data mining: practical machine learning tools and techniques includes latest material on new modernization tools and techniques.
This Second Edition of Sebastian Raschka's Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning.
As we product managers, technologists and business development professionals are looking for ways to customize solutions, products and business models for the Industrial Internet of Things, we are increasingly hungry for quality information. Most of what we read today are great forecasts of billions of connected products and how information will lead us through a "fourth industrial revolution". In other words, going from a monthly blood pressure check to continuous blood pressure monitoring and telling the patient he's having a heart attack is not enough, we must schedule and perform the surgery as well. I feel we are close to cracking the code, and my journey for valuable information lead me to a book by Dr. Timothy Chou entitled, Precision: Principles, Practices and Solutions for the Internet of Things.
My latest machine learning book has been published and will be available during the last week of July. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.
Former world chess champion Garry Kasparov is long overdue for telling his side of the story regarding his famous match with the IBM computer Deep Blue in May 1997. In the new book Deep Thinking, Kasparov and longtime writing partner Mig Greengard intertwine his experiences--before, during, and after the match--with a historical overview of chess-playing AI to produce a well-written, accessible book that provides food for thought about our future alongside increasingly intelligent machines. Many in the chess community, who may buy the book for insight into the match's outcome, will be surprised to see a side of Kasparov that the general public has not seen before--a man who has mellowed over time. Those in the artificial-intelligence and technology communities may buy this book because of the intriguing tag line "Where machine intelligence ends and human creativity begins."
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
What Advice Would You Give Your Younger Data Scientist Self? https://t.co/IVjNdrhdZC Why the'boring' part of #DataScience is actually the most interesting https://t.co/r8uR6fIgTj What Advice Would You Give Your Younger Data Scientist Self? https://t.co/IVjNdrhdZC Why the'boring' part of #DataScience is actually the most interesting https://t.co/r8uR6fIgTj What Advice Would You Give Your Younger Data Scientist Self? https://t.co/IVjNdrhdZC Why the'boring' part of #DataScience is actually the most interesting https://t.co/r8uR6fIgTj