This is the new book by Andrew Ng, still in progress. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. He is one of the most influential minds in Artificial Intelligence and Deep Learning. Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company's Artificial Intelligence Group into several thousand people. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University.
Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered in many computer science and software engineering degree programs. Even if it is, it may be taught in a bottom-up, theory-first manner, making it unclear which parts are relevant on a given project. In this post, you will discover some top introductory books to statistics that I recommend if you are looking to jump-start your understanding of applied statistics.
At The Information, Amir Efrati has a scoop: the Uber car that killed a pedestrian wheeling a bicycle across the road in Tempe, Arizona in March actually did see her. Its software just decided she was a false positive and accordingly did not stop the car. Anyone familiar with AI classifiers understands the problem, which is where to set the threshold between'ignore' and'stop'. Because it's a car instead of a photo-tagging algorithm trying to differentiate between cats and giraffes, if you set it too low -- that is, if you tell the car to stop for too many false positives -- you annoy your passengers by stopping for every shadow and plastic bag. If you set it too high -- too many false negatives -- you kill the pedestrian.
The aims of the transhumanist movement are summed up by Mark O'Connell in his book To Be a Machine, which last week won the Wellcome Book prize. "It is their belief that we can and should eradicate ageing as a cause of death; that we can and should use technology to augment our bodies and our minds; that we can and should merge with machines, remaking ourselves, finally, in the image of our own higher ideals." The idea of technologically enhancing our bodies is not new. But the extent to which transhumanists take the concept is. In the past, we made devices such as wooden legs, hearing aids, spectacles and false teeth.
Artificial intelligence has been the stuff of mad dreams, and sometimes nightmares, throughout our collective history. We've come a long way from a 15th century automaton knight crafted by Leonardo da Vinci. Within the past century, artificial intelligence has inched itself further into our realities and day to day lives and there is now no doubt we're entering into a new age of intelligence. Early computing technology ushered in a new branch of computer science dealing with the simulated intelligence of machines. In recent history, we've used AI for common tasks, such as playing against the computer in chess matches and other game play behaviors.
Dormehl starts with the 1964 World's Fair -- held only miles from where I lived as a high school student in Queens -- evoking the anticipation of a nation working on sending a man to the moon. He identifies the early examples of artificial intelligence that captured my own excitement at the time, like IBM's demonstrations of automated handwriting recognition and language translation. He writes as if he had been there. Dormehl describes the early bifurcation of the field into the Symbolic and Connectionist schools, and he captures key points that many historians miss, such as the uncanny confidence of Frank Rosenblatt, the Cornell professor who pioneered the first popular neural network (he called them "perceptrons"). I visited Rosenblatt in 1962 when I was 14, and he was indeed making fantastic claims for this technology, saying it would eventually perform a very wide range of tasks at human levels, including speech recognition, translation and even language comprehension.
Billionaire friends Elon Musk and Larry Page are reported to have a'funny' relationship. Musk apparently crashes at Page's house where they play video games together to pass they time. But the pair also argue about some major issues, including AI. A new book by renowned MIT professor Max Tegmark recounts a heated debate between the two tycoons at a glamorous party in Napa Valley. During the debate, Page accused Musk of being'speciesist' for his apparently outlandish claims that killer robots could wipe out humanity.
Feature engineering plays a vital role in big data analytics. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.
It has been widely recognized that online opinions constitute important informational sources for consumers and producers. The open nature of communication supported by social media, however, raises an important yet unsettled question of whether and how earlier opinions affect those that come after. This poster presents a model to illustrate the relationship between existing and new reviews. Based on 12,500 Amazon reviews, our choice model shows support for the idea that social contagion may be an important mechanism guiding behaviors of online reviewers. The results thus offer novel insights toward a better understanding of contagious behaviors, as well as minority influence, among social media users.
Here's a list of good books, with a brief explanation about them, which you can read to learn the essentials of machine learning: This book is the most expensive(from science perspective) and valuable book in machine learning world. It's so complete and it cover almost all aspects of machine learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. But it's disadvantages are, First, it's too long.