There are not many books on Deep Learning at the moment because it is such a young area of study.

I have heard that training deep networks can be difficult due to local minima. If you are training two neural networks with the same data. Where one of the networks is deeper (more hidden layers) than the other. Will the deeper network contain more local minima or is it impossible to say when only considering how deep the network is?

David Ledbetter, data scientist at the Children's Hospital Los Angeles, shares how his team is using TITAN X GPUs and deep learning to help provide better recommendations of drug treatments for children in their pediatric intensive care unit. To train their models, 13,000 patient snapshots were created from ten years of electronic health records at the hospital to understand the interactions between a patient's vital state, heart rate, blood pressure and the treatments they were given. By understanding the most important relationships in the data, they are then able to generate the probability of survival predictions for the patients moving forward as well as physiology predictions in order to simulate augmented treatments. David presented his research poster "Dr. Watch more scientists and researchers share how accelerated computing is benefiting their work at http://nvda.ly/X7WpH

All versions I tried are somewhat outdated. Has anyone successfully tried a somewhat recent version?

The problem of building an autonomous robot has traditionally been viewed as one of integration: connecting together modular components, each one designed to handle some portion of the perception and decision making process. For example, a vision system might be connected to a planner that might in turn provide commands to a low-level controller that drives the robot's motors. In this talk, I will discuss how ideas from deep learning can allow us to build robotic control mechanisms that combine both perception and control into a single system. This system can then be trained end-to-end on the task at hand. I will show how this end-to-end approach actually simplifies the perception and control problems, by allowing the perception and control mechanisms to adapt to one another and to the task.

To whom correspondence should be addressed; Email: artitw@gmail.com Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0.743 on the test dataset using no more than 12 temporal features for each customer. Unsupervised learning was conducted using autoencoders to better understand the reasons for customer churn. Images that maximally activate the hidden units of an autoencoder trained with churned customers reveal ample opportunities for action to be taken to prevent churn among strong data, no voice users.

In TensorFlow, during the training process of the models, the parameters are maintained in the memory as variables. When a variable is created, you can use a tensor defined as a parameter of the function as an initial value, which can be a constant or a random value.

The University of Science and Technology of China on Friday officially launched the robot "Jiajia" it invented for interactive experience. HEFEI, April 15 (Xinhua) -- A new interactive robot, named Jia Jia, was unveiled Friday by the University of Science and Technology of China (USTC) in Hefei, capital of east China's Anhui Province. Welcome!" the eye-catching robot said as it greeted the audience at the university's multi-media center. "Don't come too close to me when you are taking a picture. It will make my face look fat," Jia Jia said. Jia Jia was developed by a robot research and development team at the USTC, which also developed the model service robot "Kejia." It took the team three years to research and develop this new-generation interactive robot, which can speak, show micro-expressions, move its lips, and move its body, according to team director Chen Xiaoping. Compared to previous interactive robots, Jia Jia's eyeballs roll naturally and its speech is in sync with its lip movements, in addition to her human-like form, Chen said. Jia Jia can not cry or laugh and these are areas to be developed, Chen added. "We hope to develop the robot so it has deep learning abilities.

I think that's quite a misleading title. More appropriate to say "Adversarial images for humans". What makes adversarial examples for deep learning "intriguing" is that there are indistinguishable from the original image. We want to have behavior similar to humans and how to achieve it is an open research problem.

The "photonic time stretch" was invented by Professor Barham Jalali, who holds a patent for this technology, and its use in microscopes is just one of many possible applications. It works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly – in nanoseconds, or billionths of a second – that the images would be too weak to be detected and too fast to be digitised by normal instrumentation. The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitised at a rate of 36 million images per second. It then uses deep learning to distinguish the cancer cells from healthy white blood cells. Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data, with the goal of achieving accurate decision making.