Euclidean letter for the first quarter ended March 31, 2016; titled, "Deep Learning & Value Investing." Get the entire 10-part series on Timeless Reading in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues. In recent letters, we showed the merits of adhering to simple forms of value investing over time. In particular, we highlighted the potential that value strategies have demonstrated following periods resembling the past few years -- when value investing has underperformed the broad market and more speculative forms of investing.

Back when activity based working (ABW) was starting to gain real traction in places like Australia in 2013, we ran some research on which companies were most influential on the organisational leaders that were driving adoption of the work style. For many technology vendors and service providers the results from our more than 50 in-depth interviews with ABW adopters (now more than 250) were somewhat of a shock. The leading influencers were: #1 workpace strategy specialists; #2 interior designers; #3 peers that had adopted ABW; #4 real estate management firms and furniture providers. On the contrary, it was considered critical to get right and pretty much underpinned everything (see our checklist for guidance here). But technology providers simply didn't have a vision or narrative that was resonating with organisational leaders that also had to consider how to best use physical space while changing cultures.

Dear mid-level Wall Street worker, your computer is coming to get you. From artificial intelligence to the blockchain, the world of finance is being transformed by emerging technologies, and that will mean lots of lost jobs, according to several panelists at the Milken Institute Global Conference. Software engineers, whole technology departments, and anyone who's moving numbers from one spreadsheet to another is "going to get decimated," Daniel Nadler, chief executive officer of Kensho Technologies Inc., said Monday on a panel about technology's impact on Wall Street. Kensho provides data analytics to banks. "You have probably the most expensive utility infielders in the world here," earning six figures for work of little real value, Nadler said. "Those people are not going to have jobs.

Bayesian Optimization helped us find a hyperparameter configuration that is better than the one found by Random Search for a neural network on the San Francisco Crimes dataset. People who are familiar with Machine Learning might want to fast forward to Section 3 for details. The code to reproduce the experiments can be found here. Hyperparameter tuning may be one of the most tricky, yet interesting, topics in Machine Learning. For most Machine Learning practitioners, mastering the art of tuning hyperparameters requires not only a solid background in Machine Learning algorithms, but also extensive experience working with real-world datasets.

Please make Pull Requests for good resources, or create Issues for any feedback! Visual QA is a continuation of deep learning's efforts in image captioning and NLP-based question answering. The goal is for a neural network to take in an image with a question and output good answers. Two interesting events occurred in late 2014. The first would be efforts in image captioning.

Computing Microsoft and Google Want to Let Artificial Intelligence Loose on Our Most Private Data New ways to use machine learning without risking sensitive data could unlock new ideas in industries like health care and finance. April 19, 2016 Sponsored by The recent emergence of a powerful machine-learning technique known as deep learning has made computing giants such as Google, Facebook, and Microsoft even hungrier for data. It's what lets software learn to do things like recognize images or understand language. Yet many problems where deep learning could be most valuable involve data that is hard to come by or is held by organizations that are unwilling to share it. And as Apple CEO Tim Cook puts it, some consumers are already concerned about companies "gobbling up" their personal information.

Mark Zuckerberg believes that within a time span of five to 10 years, AI could surpass the capabilities of human beings. Here are additional details on what the founder of Facebook believes will happen in the next decade. Mark Zuckerberg explains that his company has already been focusing on AI through machine learning, computer vision and natural language processing and speech. To take a stroll down memory, earlier this month, we saw an iOS feature called'automatic alternative text' that uses object recognition technology to provide spoken descriptions of Facebook photos to people who are visually impaired. Additionally, Facebook also unveiled a new bot and chatbot technology as part of its Messenger platform. These bots are currently in beta and their primary objective will be to enable businesses using the platform to provide automated information and help to online customers.

Now that's a word that packs a punch! Machine learning is hot stuff these days! And why won't it be? Almost every "enticing" new development in the field of Computer Science and Software Development in general has something related to machine learning behind the veils. The Amazon product recommendation you just got was the number crunching effort of some Machine Learning Algorithm).

The quest for artificial intelligence (AI) began over 70 years ago, with the idea that computers would one day be able to think like us. Ambitious predictions attracted generous funding, but after a few decades there was little to show for it. But, in the last 25 years, new approaches to AI, coupled with advances in technology, mean that we may now be on the brink of realising those pioneers' dreams.

Humans use their vision to see things and then they interpret those via their brain. Similarly, computer vision is to make computers perceive, process and understand visual data such as images and videos. The ultimate goal of computer vision is to model, replicate, and more importantly exceed human vision using computer software and hardware at different levels. It needs knowledge in computer science, electrical engineering, mathematics, physiology, biology, and cognitive science. Computer vision is, in some ways, the inverse of computer graphics.