Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance.

Machine Learning and Deep Learning has brought the future here. Predicting the future has always been the most sought after skill in this world. How much money could you make if you could predict the price of a stock or if you could predict which color will be in fashion six months later? You can predict almost anything that you wish. The future will be in your own palms.

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

Summary: Researchers from UC Berkeley have developed a new technique that uses deep networks and AI to colorize images. UC Berkeley computer scientists develop smarter, enhanced data-driven colorization system for graphic artists. For decades, image colorization has enjoyed an enduring interest from the public. Though not without its share of detractors, there is something powerful about this simple act of adding color to black and white imagery, whether it be a way of bridging memories between the generations, or expressing artistic creativity. However, the process of manually adding color can be very time consuming and require expertise, with typical professional processes taking hours or days per image to perfect.

Artificial Intelligence has become a very popular term today. There is sure to be at least one article in the newspaper daily on the revolutionary advancements made in the field. But, there seems to be some confusion about what AI really is. Will the Terminator movie actually come true? Or is it something that has crept into our daily lives without us even realizing it? This article will give you a broad understanding on the buzzwords associated with AI, its applications, the careers & opportunities it has and its future.

Time Series is defined as a set of observations taken at a particular period of time. For example, having a set of login details at regular interval of time of each user can be categorized as a time series. On the other hand, when the data is collected at once or irregularly, it is not taken as a time series data. Time series is a sequence that is taken successively at the equally pace of time. It appears naturally in many application areas such as economics, science, environment, medicine, etc.

Every time a new tool or app is invented, a new word follows. So, let's tackle two that have been flying around our heads for the past few years: Machine Learning (ML) and Deep Learning (DL). Techies, business gurus, and marketers love these words and throw them around whether or not they understand the differences. Side Note: We know that this topic is old news, it's discussed continuously. Which is why we had to write about it, clearly it's not being fully understood because all the current content out there is either too simple or too complicated.

Researchers from the Byers Eye Institute at Stanford University have found a way to use artificial intelligence to fight a complication of diabetes that affects the eyes. This advance has the potential to reduce the worldwide rate of vision loss due to diabetes. In a study published online in Ophthalmology, the journal of the American Academy of Ophthalmology, the researchers describe how they used deep-learning methods to create an automated algorithm to detect diabetic retinopathy. Diabetic retinopathy (DR) is a condition that damages the blood vessels at the back of the eye, potentially causing blindness. "What we showed is that an artificial intelligence-based grading algorithm can be used to identify, with high reliability, which patients should be referred to an ophthalmologist for further evaluation and treatment," said Theodore Leng, M.D., lead author.

Insilico Medicine develops the advanced artificial intelligence (AI) algorithms to study the ageing processes and discover new interventions in ageing, many of these molecules aim to induce the expression of certain genes involved in the endogenous repair processes to slow down and even reverse some of the aging-associated diseases. By applying a specific branch of artificial intelligence called Deep Learning (DL) on multi-modal data, the company aims to discover molecules that can stimulate the repair of the DNA. The objective of this collaboration is to increase health span for everyone on the planet. "Many of the diseases of aging are associated with the failure of the DNA repair mechanisms. The ageing processes accelerate as the DNA repair mechanisms lose function. The collaboration with Insilico Medicine will allow us to find the molecules that repair DNA and prevent accelerated ageing", said the head of the biology of ageing lab and Assistant Professor Morten Scheibye-Knudsen, Center for Healthy Ageing.