Last week Google announced that it will be making its artificial intelligence (AI) and machine learning (ML) courses available to everyone. In order to help, everyone understands how AI can solve challenging problems and to make learning resources available, Google has created a new platform called Learn with Google AI. Zuri Kemp, who leads Google's machine learning education effort, wrote in a blog post that the aim of this initiative is making AI and its benefits accessible to everyone. "Part of Google AI's mission is to help anyone interested in machine learning succeed -- from researchers to developers and companies, to students," wrote Kemp. Learn with Google AI website provides ways to learn about core ML concepts, develop and hone ML skills, and apply ML to real-world problems.

"Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed" -- Arthur Samuel, 1959. Machine learning and artificial intelligence have been a rising field of research in both the corporate and the academic world. Machine learning proves to be incredibly powerful when it comes to making predictions or calculated suggestions that are based on large amounts of data. If an individual wants to master machine learning, how do you start and from where? In order to learn about Machine Learning, one not only needs a keen interest in it but also have the right resources.

Neural networks and deep learning is a great website that goes step by step into neural network architectures, loss functions used etc. It should be very easy to read if you have a bit of math background. CS231N lecture notes from here: CS231n Convolutional Neural Networks for Visual Recognition (notes), and CS231n: Convolutional Neural Networks for Visual Recognition (lecture slides) Once you are familiar with the basics and the terminology (which is not much I should say), you can read tutorials of deep learning software such as Caffe (Caffe Deep Learning Framework, read through the Example section), and Theano (Deep Learning Tutorials). If you are feeling adventurous, you can also look through the proceedings and submitted papers for conferences such as ICLR and NIPS. Deep learning being a fast growing area, once you understand the basics it should not be much of an effort to understand what is being talked about in most of the papers.

MANILA – English school chain ECC Co. will launch a Japanese course in the Philippines in June in partnership with a local college amid growing interest in the language among Filipinos. The Osaka-based firm and the University of Perpetual Help plan to provide a 6-month e-learning program, including a weekly supplementary lecture, for 35,000 pesos (¥72,000), targeting employees of Japanese affiliates and those planning to study and work in Japan, the company said. ECC's first Japanese-language course overseas aims to cater to an increasing number of Filipinos taking the Japanese Language Proficiency Test, a widely used exam for evaluating and certifying the language proficiency of nonnative speakers, it said. In 2017, a record 14,062 Filipinos took the exam, up 21 percent from the previous year, while the tally for all examinees topped 1 million for the first time, according to the Japan Foundation, which administers the test. The private university, founded in 1975, has three campuses in the south of Manila with about 2,000 employees and some 18,000 students, according to ECC.

Linear algebra is a sub-field of mathematics concerned with vectors, matrices, and linear transforms. It is a key foundation to the field of machine learning, from notations used to describe the operation of algorithms to the implementation of algorithms in code. Although linear algebra is integral to the field of machine learning, the tight relationship is often left unexplained or explained using abstract concepts such as vector spaces or specific matrix operations. In this post, you will discover 10 common examples of machine learning that you may be familiar with that use, require and are really best understood using linear algebra. In this post, we will review 10 obvious and concrete examples of linear algebra in machine learning.

Give us your feedback Thank you for your feedback. Do grills have girlish associations? A study has revealed how an artificial intelligence (AI) algorithm learnt to associate women with pictures of the kitchen, based on a set of photos where the people in the kitchen were more likely to be women. As it reviewed more than 100,000 labelled images from around the internet, its biased association became stronger than that shown by the data set -- amplifying rather than simply replicating bias. The work by the University of Virginia was one of several studies showing that machine-learning systems can easily pick up biases if their design and data sets are not carefully considered.

The post coincides topically with last years' first annual Conference on Robot Learning as well as the workshop on Challenges in Robot Learning at NIPS2017, the latter we had the pleasure of co-organising together with colleagues from Oxford, DeepMind, and MIT. The events, as well as this post, cover current challenges and potentials of learning across various tasks of relevance in robotics and automation. In this context, similar to the long-term discussion on how much innate structure is optimal for artificial general intelligence, there is the more short-term question of how to merge traditional programming and learning (not sure if I prefer the branding as differentiable programming or software 2.0) for more narrow applications in efficient, robust and safe automation. The question about structure as beneficial or limiting aspect becomes arguably easier to answer in the context of robotic near-term applications as we can simply acknowledge our ignorance (our missing knowledge about what will work best in the future) and focus on the present to benchmark and combine the most efficient and effective directions. Existing solutions to many tasks in mobile robotics, such as localisation, mapping, or planning, focus on prior knowledge about the structure of our tasks and environments. This may include geometry or kinematic and dynamic models, which therefore have been built into traditional programs. However, recent successes and the flexibility of fairly unconstrained, learned models shift the focus of new academic and industrial projects. Successes in image recognition (ImageNet) as well as triumphs in reinforcement learning (Atari, Go, Chess) inspire like-minded research. As the post has become a bit of a long read, I suggest to read it like a paper: intro, discussion & conclusions and then - only if you did not fall asleep after all - the rest. Similar to scientific papers, some paragraphs will require basic familiarity with the field. However, a coarse web search should be enough to illustrate most unexplained terminology. Additionally, to keep this engaging, I have added some of my favourite recent videos highlighting interesting research for each section. Finally, this is a high-level review with more details to be found in the respective references, which just represent a small subset of available work in each field, chosen based on personal interest as well as shameless self-promotion of our work.

Welcome to Tech Explorations Arduino Step by Step Getting Serious, where you will extend your knowledge of Arduino components and techniques and build up new skills in the largest, and the most comprehensive course on the Web! Arduino is the world's favorite electronics learning and prototyping platform. Millions of people from around the world use it to learn electronics, engineering, programming, and create amazing things, from greenhouse controllers to tree climbing robots remotely controlled lawnmowers. It is a gateway to a career in engineering, a tool for Science, Technology, Engineering, and Mathematics education, a vehicle for artistic and creative expression. The course is split into 40 sections and over 250 lectures spanning more than 30 hours of video content. In each section, you will learn a specific topic.

Al Martin: Hi folks, this is Al Martin from Making Data Simple, the series, if you will. Today I have Jean-Francois Puget. Jean-Francois Puget: Yes, you did great. You passed your French test. Al Martin: All right, good, I'm going to give you the [name] JFP from now on, is that all right? So JFP is the distinguished engineer for machine learning and optimization, that's the topic today and we're going to go into that. I also have with me [Steve Moore], who is a senior content designer and storage strategist. Al Martin: So Steve wanted to join the conversation, ask a few questions. So he'll ask the intelligent questions, I will ask the normal, blockhead questions, if you will. So, thank you for being here. We've done a lot, well we've done at least, I think two podcasts on machine learning. We've done one on machine 1.15 learning for dummies, one for IBM machine learning, how to [help], if you haven't heard those, go back, so we can't do enough, and I notice that on your title JFP is machine learning and optimization.

Bitcoin's mid-December boom has sparked a renewed interest in cryptocurrency investment across the world - and some extremely strange goings-on as entrepreneurs speculate on the best way to capitalise on the digicoin gold rush. The market leader's value stormed to an all-time high of $19,850 (£14,214) in the run-up to Christmas but has since dropped back to its customary $10,000 (£7,190) mark. Competitors like ethereum, litecoin, ripple and bitcoin cash have all quietly prospered out of the limelight - but cryptocurriences remain a volatile proposition. Yesterday saw all but two of CoinMarketCap's top 50 cryptos decline in value, thought to be a reaction to the US Securities and Exchange and Commission declaring that all trading platforms will ultimately need to be registered and regulated to protect consumers. All of this excitement has led to a number of bizarre developments as businesses (and scammers) think outside the box about the best way to enter a market worth a combined $405bn (£292bn).