Welcome to the "mom report" (Hi mom!); if jargon and mumbo jumbo are more your style then maybe this is what you're after, otherwise enjoy! I'm already counting the days (four, at the moment) until Term 2 begins and trying to decide the best way to sustain my momentum, starting with this here recap of Project 5 -- Vehicle Detection. The interesting thing to me about this project, in particular, was that it sort of occupied the middle ground between the first and fourth projects and the second and third projects. The first and fourth projects used old-school computer vision techniques and explicitly defined steps to produce an output (highlighting the location of lane lines), whereas the second and third projects employed deep learning's hot-ass newness (I might have to trademark that) to sort of let the program figure out the rules on its own based on a ton of examples. The goal of the Vehicle Detection project was to identify vehicles in dashcam video. While there are already deep learning implementations (e.g.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation.

Jessica is a business and finance writer, focusing on impact investing, social entrepreneurship and economic development. She previously reported for financial publications covering the global private equity, real estate and insurance markets. "Quality education will always require active engagement by human teacher," write researchers from the Stanford One Hundred Study on Artificial Intelligence. But artificial intelligence will inform the teaching processes of the future as pressure builds on educators to "contain costs while serving a larger number of students and moving students through school more quickly." This week, ImpactAlpha is extracting nuggets from Stanford's century-long effort to understand AI's long-term possibilities and dangers.

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in the chart below to see its documentation.

Interview Sure, algorithms are insanely useful, but we need to watch we don't become complacent and unable to question them, University College London's Dr Hannah Fry warned in an interview with The Register. Dr Fry is a lecturer in the mathematics of cities at the Centre for Advanced Spatial Analysis at UCL, where her research "revolves around the study of complex social and economic systems at various scales, from the individual to the urban, regional and the global, and particularly those with a spatial element." While not engaged in research, however, Dr Fry is quickly becoming one of the UK's favourite mathematicians, known for her work on BBC 4's The Joy of Data, as well as her popular TED talk, 'The Mathematics of Love', which applied statistical and data-scientific models to dating, sex and marriage. Chatting to The Register ahead of DataFest2017, the inaugural week-long data science festival in Edinburgh, Dr Fry said she thought the event was going to be "a lot of fun". It's something people really need to address, and having so many excellent people together in a room at once; it's going to be a great few days." "Data science as a field has exploded over the past five years," because there's "much more access to data now" said Dr Fry, noting that with "sensors, IoT, with us living more of our lives online" there's now "very little that is untouched by data". We "realised a few years ago how much data there was," Dr Fry said. "I think the whole thing is very exciting.

They were also discussed in 2015 in the Computing at School (CAS) framework and guide for teachers to enable teachers in the U.K. to incorporate computational thinking into their teaching work.10 CSTA/ISTE and CAS also provide pedagogical approaches to embed these capabilities across the curriculum in elementary and secondary classes. For example, CSTA/ISTE describes how the nine core computational thinking concepts and capabilities could be practiced in science classrooms by collecting and analyzing data from experiments (data collection and data analysis) and summarizing that data (data representation). Computational thinking is often mistakenly equated with using computer technology. Algorithms are central to both computer science and computational thinking.

Images produced with innovation engines were not only accepted to a selective art competition and displayed at the University of Wyoming Art Museum, but they also were among the 21% of submissions that won an award. It is not unusual to hear a student is taking an advanced placement computer science (AP CS) course these days, but eyebrows raise when Jackeline Mendez tells people about it, because Mendez is a senior at Boston Arts Academy where, as the name implies, the emphasis is on the arts. "I had a free block, and I was surprised how [computer science is] more than just systems and machines and the Internet," explains Mendez, who plans to major in physics in college. "People have a mind set that it's machines, but it's really not. It's what the world is right now. We use computer science for everything."

The Penn State EdTech Network is excited to announce that the top 10 proposals have received $5,000 each in seed money for development of their ideas for the Nittany Watson Challenge. The challenge encourages teams of Penn State students, faculty, and staff to leverage IBM Watson to improve the student experience and the selected proposals did not disappoint. "We received 39 incredibly strong proposals in the initial phase of the Nittany Watson Challenge, and what emerged were 10 proposals that exemplify the best of the creative and innovative energy across our University," said Brad Zdenek, Innovation Strategist for the Penn State EdTech Network. "The roughly 50 team members in these projects are spread throughout our colleges and campuses, representing undergraduate students, graduate students, faculty, and staff. What they have in common is that each is focused on leveraging the capabilities of Artificial Intelligence to create a better experience for both current and prospective students at Penn State."

Even with universities now offering master's degree programs in data science (as opposed to only PhDs), that still won't produce enough pros. "You need teams of data scientists who can actually understand neural networks and tweak them," says Matthew Zeiler, who founded the visual recognition startup Clarifai in 2013 after earning his PhD in computer science. Machine learning, which digests huge amounts of data to identify patterns, is the hottest branch of AI today, with applications as diverse as organizing cell-phone photos, teaching computers to drive autonomous cars, and studying cancer. As Gilbert explains, "No matter how many [people] we train--and other companies are doing the same thing--it's just not enough to make machine-learning AI mainstream." Which is why the industry is moving toward a point-and-click AI revolution.