In this article, we are going to list the top 10 Machine Learning books that you should read before you start with it. As you know, Machine Learning is a combination of statistic techniques that help computers learn stuff done by humans. This way we are achieving some results with superhuman precision. Now, as we all know, in order to understand the basics of Machine Learning, it's nice to have some knowledge in different areas of "Mathematics". Before you start reading these top 10 books before starting with Machine Learning, we want to show you two other related articles that you will find very helpful.
The spacesuited figure on the cover makes it look like science fiction, the opening liability disclaimer would fit right into to an end-user licence agreement, the index is sandwiched between a 40-page bibliography and a handy list of abbreviations, from CAPTCHA to YOLO, that you probably know but might like an reminder of, and the whole book is covered by a Creative Commons licence. So how does this unusual approach to publishing stand up? The introduction to Exponential Progress purports to be written at the back end of the 21st century, reframing the usual'outhouse to zero-gravity toilet in 70 years' reference to the speed of progress as a 300-year jump from no electricity to'outer-terrestrial colonies' (along with hints about a civilisation of AIs). By promising to explain to fictional readers some eight decades in the future how we got there, author Farabi Shayor gets licence to cover the state of the art across a range of current and bleeding-edge technologies -- virtual reality, electric and self-driving cars, AI (both software and'brain-like' chips), the singularity, brain-computer interfaces, CRISPR and synthetic biology -- for a general audience. Although the book promises to explore the dangers of emerging technology and whether the pace of innovation is beyond human control, the writing is often unstintingly optimistic.
If you are interested in learning Data Science with R, but not interested in spending money on books, you are definitely in a very good space. There are a number of fantastic R/Data Science books and resources available online for free from top most creators and scientists. Here are such 13 free 20 free (so […]
With the proliferation of information technologies and data among us, cybersecurity has become a necessity. Machine learning helps organisations by getting insights from raw data, predicting future outcomes and more. For a few years now, such utilisation of machine learning techniques has been started being implemented in cybersecurity. It helps in several ways, including identifying frauds, malicious codes and other such. In this article, we list down the top eight books, in no particular order, on machine learning In cybersecurity that one must-read.
Artificial Intelligence (AI) has taken the world by storm. Almost every industry across the globe is incorporating AI for a variety of applications and use cases. Some of its wide range of applications includes process automation, predictive analysis, fraud detection, improving customer experience, etc. To learn more about AI and it's concepts, you can start by reading the Top Artificial Intelligence Books for self-learning. AI is being foreseen as the future of technological and economic development.
Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. If artificial intelligence will destroy humanity, it probably won't be through killer robots and the incarnation--it will be through a thousand paper cuts. In the shadow of the immense benefits of advances in technology, the dark effects of AI algorithms are slowly creeping into different aspects of our lives, causing divide, unintentionally marginalizing groups of people, stealing our attention, and widening the gap between the wealthy and the poor. While we're already seeing and discussing many of the negative aspects of AI, not enough is being done to address them. And the reason is that we're looking in the wrong place, as futurist and Amy Webb discusses in her book The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity. Many are quick to blame large tech companies for the problems caused by artificial intelligence.
Data science is an interdisciplinary field that combines methods from statistics, mathematics, computer science, and information science to extract insights and knowledge from data. As big data continues to surge across industries, being a data scientist is one of the most highly demanded jobs on the market. In this post, we will discuss two free online resources that are great for data science beginners. This is an introductory text available for free online as an eBook. It is written by statistics professors at Stanford University, the University of Washington, and the University of Southern California.
Today, humans may outperform AI in hazardous activities (e.g., road traffic), but there will come a time when AI surpasses humans, and then the question might be whether a reasonable person could have used AI to avoid damage. However, the principle of AI legal neutrality does not mean that AI and people must be treated equally, or that AI should enjoy the same rights as humans. Therefore, the author argues that AI should be recognized as an entity that morally deserves rights and can, for example, claim tangible or intangible property rights "only" if this would exceptionally benefit people. Furthermore, he states that AI legal neutrality should not come at the expense of transparency and accountability.
Description: This book provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Be prepared, it is a big book!. Also, check out their great probability cheat sheet here.