Before performing radiation therapy, radiation oncologists first carefully review medical images of a patient to identify the gross tumor volume -- the observable portion of the disease. They then design patient-specific clinical target volumes that include surrounding tissues, since these regions can hide cancerous cells and provide pathways for metastasis. Known as contouring, this process establishes how much radiation a patient will receive and how it will be delivered. In the case of head and neck cancer, this is a particularly sensitive task due to the presence of vulnerable tissues in the vicinity. Though it may sound straightforward, contouring clinical target volumes is quite subjective.

Having the sense to take a shortcut, the most direct route from point A to point B, doesn't sound like a very impressive test of intelligence. Yet according to a new report appearing today in Nature, in which researchers describe the performance of their new navigational artificial intelligence, the system's ability to explore complex simulated environments and find the shortest route to a goal put it in a class previously reserved for humans and other living things. The surprising key to the system's performance was that while learning how to navigate, the neural net spontaneously developed the equivalent of "grid cells," sets of brain cells that enable at least some mammals to track their location in space. For neuroscientists, the new work seems to offer important clues about how grid cells in living brains make us better navigators. It also shows how neural nets could contribute greatly to future neuroscience studies: Neil Burgess, a cognitive neuroscientist at University College London who was not involved with the study, suggested that the systems should "provide fertile ground for understanding how and why the human brain works as it does."

Deep Learning is part of a broader family of machine learning methods based on learning data representations. Deeplearning4j is a Deep Learning programming library written in Java and the Java Virtual Machine (JVM) and is a computing framework with wide support for Deep Learning algorithms. In this course, you start by installing Deep Learning software for Java. You learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. The course will take you into Neural Networks, working with Perceptron, XOR, and Gradient Descent on code examples.

Richard Sheng is the co-founder of QuantumViz, a big data visualization software company that allows data scientists and analysts to find insights in massive data sets, and create amazing data stories in 3D, VR, or AR. Richard worked in data science previous to taking the Deep Learning course with NYC Data Science Academy but now works as the CEO and co-founder of QuantumViz, which was his final project of the course. Before NYCDSA, Richard spent four years at TE Connectivity in Strategy and Business Development, working on strategic data science projects that have created multi-million dollar revenue impact. Prior to TE, Richard was an Investment Banker at Nomura Securities, supporting technology, media, and financial technology institutions. Before Nomura, Richard was a Principal Consultant in the SAP Data Science team, working with clients like Disney, Altria, FedEx and many other large corporations across industries.

Machine learning – The ability for computers to improve functionality based on a variety of algorithms including pattern and text recognition. Over time, as it has more reference data, the machine learns to become more efficient. Natural-language processing – A process that deals with a computer's ability to analyze language through speech recognition, semantics and syntax. Just like a human learns a language through listening and reading while understanding the context, computers can attain a similar capability. Deep learning – A broader version of machine learning, deep learning is the ability for a computer to process various pieces of information the way a human would to make informed decisions and judgements.

Scala has emerged as an important tool for performing various data analysis tasks efficiently. This video will help you leverage popular Scala libraries and tools and perform core data analysis tasks with ease. This course will introduce you to Deeplearning4j; you will start with tasks such as integrating with Spark and Linear Regression with Deep Learning. Then you will make use of popular Scala libraries such as Breeze to plot your data. There is also a special focus on using Bokeh to plot your data.

NVIDIA and Arm just announced that they are partnering to bring deep learning inferencing technology to mobile, consumer electronics and the Internet of Things devices. As a result of the partnership, NVIDIA and Arm will integrate NVIDIA's open-source Deep Learning Accelerator (NVDLA) architecture into Arm's Project Trillium platform for machine learning. "Accelerating AI at the edge is critical in enabling Arm's vision of connecting a trillion IoT devices," said Rene Haas, executive vice president, and president of the IP Group, at Arm. "Today we are one step closer to that vision by incorporating NVDLA into the Arm Project Trillium platform, as our entire ecosystem will immediately benefit from the expertise and capabilities our two companies bring in AI and IoT." The collaboration is meant to simplify integration of AI into IoT device and chip companies. Arm's Project Trillium is integral to the Arm Heterogenous ML compute platform, and leverages Arm ML processors, the Arm object detection (OD) processor, and open-source Arm NN software. NVIDIA's NVDLA is a free, open architecture meant to promote a standard method to design deep learning inference accelerators.

Object detection is a domain that has benefited immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet. Object detection is a domain that has benefited immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet. For the past few months, I've been working on improving object detection at a research lab.

I like where this line of thoughts goes: functional programming means functional composability. Decoupled deep learning modules is an exciting research area: Decoupled Neural Interfaces using Synthetic Gradients has shown, for example, very promising results [22] I am not sure if the term Differentiable Programming will stick around. The risk of confusion with Differential Dynamic Programming is high. The idea, on the other hand, is intriguing. Very intriguing and I am very happy to see projects such as Tensorlang [17] gaining traction. Wired argued that soon we won't program computers. We'll train them like dogs.

I just won a gold medal on Talking Data competition on Kaggle, finishing 6th. My approach and solution is described here. The part that triggered most interest from readers is where I used matrix factorization techniques to generate additional features. Before that, let me briefly explain what this competition was about. To support your modeling, they have provided a generous dataset covering approximately 200 million clicks over 4 days!