This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy).

The next Machine Intelligence Summit will take place in New York on 9-10 November, to explore how AI will impact transport, manufacturing, healthcare, retail and more. Early Bird tickets are now available for this event - for more information and to register, visit the event page here. See the full events list here for events focused on AI, Deep Learning and Machine Intelligence taking place in London, Amsterdam, Boston, San Francisco, New York and Singapore.

Google announced today that it has acquired Paris-based Moodstocks, a startup that has developed machine learning technology to bolster the image recognition features on smartphones. "We continue to pursue our machine learning and research efforts," wrote Vincent Simonet, head of the research and development team for France Google, "and Moodstocks is the latest proof of our commitment to this area." Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription. The terms of the deal were not disclosed.

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia). "Nat ur al Lan guage Pro cessing is a field that cov ers com puter un der stand ing and ma nip u la tion of hu man lan guage, and it's ripe with pos sib il it ies for news gath er ing," Anthony Pesce said in Natural Language Processing in the kitchen. "You usu ally hear about it in the con text of ana lyz ing large pools of legis la tion or other doc u ment sets, at tempt ing to dis cov er pat terns or root out cor rup tion." NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way.

Jana Eggers will be speaking on "How to Scope an AI Project" at the upcoming O'Reilly Artificial Intelligence Conference. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Jana Eggers, CEO of Nara Logics. Most recently she has been helping companies across many industries adopt AI technologies as a way to enable a range of intelligent data applications.

What do you see when you think about artificial intelligence? With a new competition, XPRIZE and IBM Watson challenge us to think slightly more pragmatically (or at least less apocalyptically dystopian) about the implications of artificial intelligence research on the future of humanity. To date, more than 1,000 people have registered to form teams with plans to tackle such issues as health, climate, transportation, space travel, robots, city planning, surgery, education and even civil rights. Because the competition is an open challenge, teams are expected to come up with criteria on which they will ultimately be judged in 2020. Some teams may be backed by corporations or angels, while others may go it alone as a team.

Artificial intelligence (AI) has been around for decades, but only recently has it begun to emerge as a viable field with massive commercial opportunity. Data is the fuel that feeds AI and is opening opportunities that we can only have imagined until now. And, large enterprises are waking up to this opportunity. Findings from our newly published research report, "Outlook on Artificial Intelligence in the Enterprise 2016" indicate major changes as to the perception and use of AI-powered solutions. The survey results clearly speak to the power of the partnership.

Apple has acquired a machine learning and artificial intelligence (AI) startup called Turi, which is based in Seattle, according to reports. Apple on its part has not commented on the deal, and its statement only said the company does make these acquisitions from time to time, which is the standard Cupertino-line on such report. The deal was first reported by GeekWire, which said Apple is paying close to 200 million for the company. According to a report on ReCode, Turi was earlier known as both GraphLab and Dato, and is used by companies like Pandora used to power recommendations. The company was founded was Carlos Guestrin, a University of Washington professor, but he too refused to comment on the deal.

DESCRIPTION Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon's customers? Do you want to build advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify?

Education technology has the power to revolutionize education but with the integration of artificial intelligence, experts believed that it can be more beneficial, particularly in higher education and online learning. In an era where modern technology has become a valuable influence in the lives of humans, it's safe to assume that technology will be able to enhance the learning experience of educators and students, especially in higher education and online learning. As experts combined education technology (EdTech) and artificial intelligence (AI), a powerful tool to potentially transform education has been born. Due to the pervasiveness of technology today, the way students communicate and entertain themselves have changed. But some experts believed that the implementation of education technology alone in schools, colleges and universities across the nation is not enough to revolutionize education.