Serengil received his MSc in Computer Science from Galatasaray University in 2011. He has been working as a software developer for a fintech company since 2010. Currently, he is a member of AI and Machine Learning team as a Data Scientist. His current research interests are Machine Learning and Cryptography. He has published several research papers about these motivations.

Intel AI Lounge – Unleashing the Potential for Everyone Panel at SXSW Intel Business Panelists Dr. Dawn Nafus, Intel, Lila Ibrahim, COO, Coursera and Pratool Bhartri, AI Graduate Student Ambassador, Intel discuss how Intel is engaging with scientists, students and developers to increase momentum behind broader adoption of AI solutions. Artificial intelligence innovations will bring benefits to multiple industries, and to society as a whole in the way we lead our everyday lives. AI will change our lives for the better as machines learn, reason, act and adapt -- transforming industries by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems. Intel can offer crucial technologies to drive the AI revolution, but ultimately we must work together as an industry -- and as a society -- to achieve the ultimate potential of AI. About Intel Business: Get all the IT info you need, right here.

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico. Here we revisit the problem of supervised learning in temporally coding multi-layer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three factor learning rule capable of training multi-layer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike-time patterns.

A prospective student, looking forward to major in English, calls the financial aid office of her dream college. After two rings, an automated voice asks her what does she need help with. On her inquisitiveness about the financial aid procedure, the voice on the other end runs her through the nuances of the process in a systematic manner. Despite the student's repeated queries regarding the same thing, there is no sign of tiredness or irritation in the voice. The student, eventually, hangs up satisfied and pleased with the agent's efficiency. Imagine the same happening during the peak season in colleges.

I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software (ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India - Not still cheap), availability of data, good books and MOOCs. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. This week we're featuring Mike's interview with Felipe Chavez, Co-Founder and CEO of Kiwi. Kiwi is a mobile robot company delivering food to hungry college students across University of California, Berkeley's campus. Listen to Felipe explain some of the challenges Kiwi faces when deploying their robots.

Sure, there are currently all sorts of options for learning machine learning. You've got your more traditional methods like textbooks. You've got your fancy newfangled approaches like MOOCs and video lectures on YouTube. Podcasts, blogs, Quora questions (and sometimes answers), and research papers abound! But Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.

Before we get worried about the possibility of a robot uprising, we probably have to worry about our jobs first. Since machines could take millions of jobs the next few years, Google has launched a new initiative to help people in the US and around the globe learn new skills they can use to start a new career or to grow their business. Company chief Sundar Pichai has announced the project called "Grow with Google" at an event in Pittsburgh. He said that the tech titan understands "uncertainty and even concern about the pace of technological change" but that it believes "that technology will be an engine of America's growth for years to come." The Grow with Google website houses several programs both teachers and students (of any age) can use.

White-collar automation has become a common buzzword in debates about the growing power of computers, as software shows potential to take over some work of accountants and lawyers. Artificial-intelligence researchers at Google are trying to automate the tasks of highly paid workers more likely to wear a hoodie than a coat and tie--themselves. In a project called AutoML, Google's researchers have taught machine-learning software to build machine-learning software. In some instances, what it comes up with is more powerful and efficient than the best systems the researchers themselves can design. Google says the system recently scored a record 82 percent at categorizing images by their content.