Microsoft's deep learning toolkit for speech recognition is now on GitHub.

DLTK is a neural networks toolkit written in python, on top of TensorFlow. It is developed to enable fast prototyping with a low entry threshold and ensure reproducibility in image analysis applications, with a particular focus on medical imaging. Its goal is to provide the community with state of the art methods and models and to accelerate research in this exciting field. If you use any application from the DLTK Model Zoo, additionally refer to the respective README.md To ease into the subject, we wrote a quick overview blog entry (12 min read) for the new TensorFlow blog.

Project OpenDR which PAL Robotics is a project partner in has released a toolkit for deep learning in robotics – one of the first of its kind, that provides over 20 methods for human pose estimation, face detection, face recognition, facial expression recognition, object detection and more. The aim is to enable a wide range of capabilities in robotics more easily using the toolkit. Advances in deep learning have brought about significant developments in technology such as self-driving cars and algorithms that are able to understand and answer questions. However, the application of deep learning in robotics creates challenges in learning, reasoning, and embodiment problems and research questions that are often not addressed by the computer vision and machine learning communities. The team at PAL Robotics will use multiple capabilities developed in the OpenDR toolkit on the TIAGo robot.

Researchers from Nvidia and Harvard are publishing research this week on a new way they've applied deep learning to epigenomics -- the study of modifications on the genetic material of a cell. Using a neural network originally developed for computer vision, the researchers have developed a deep learning toolkit that can help scientists study rare cell types -- and possibly identify mutations that make people more vulnerable to diseases. The new deep learning toolkit, called AtacWorks, "allows us to study how diseases and genomic variation influence very specific types of cells of the human body," Nvidia researcher Avantika Lal, lead author on the paper, told reporters last week. "And this will enable previously impossible biological discovery, and we hope would also contribute to the discovery of new drug targets." AtacWorks, featured in Nature Communications, works with ATAC-seq -- a popular method for finding the parts of the human genome that are accessible in cells.

Arabic is the 4th most-used language on the Internet, and its growing presence on social media is providing ample resources for the study of Arabic-language online communities at scale. There are however few tools currently available that can derive valuable insights from this data for decision making, guiding policies, aiding in responses, etc. Is that about to change? The performance of natural language processing (NLP) systems has dramatically improved on tasks such as reading comprehension and natural language inference, and with these advances have come many new application scenarios for the tech. Unsurprisingly, English is where most NLP R&D has been focused.

Why are the results of the latest models so difficult to reproduce? Why is the code that worked fine last year not compatible with the latest release of my deep learning framework? Why is a baseline benchmark meant to be straightforward so difficult to set up? In today's world, these are the challenges faced by Natural Language Processing (NLP) researchers. Let's take the case of a hypothetical PhD student.

DLTK is an open source library that makes deep learning on medical images easier. DLTK comes with introduction tutorials and basic sample applications, including scripts to download data.

Author: Mu Li, Principal Scientist at Amazon Translated from: https://zh.mxnet.io/blog/gluon-cv Someone once asked me what was the hardest thing to do when developing MXNet. I would not hesitate to say that replicating experimental results from papers is the most difficult part. The heroes in the three examples above are top-level researchers in the field of deep learning, but it is still very easy to spend a lot of precious time on some subtle experimental details. A model usually has tens to hundreds of layers and can take several hours to train.

Machine learning is transforming business. But even as the technology advances, companies still struggle to take advantage of it, largely because they don't understand how to strategically implement machine learning in service of business goals. Hype hasn't helped, sowing confusion over what exactly machine learning is, how well it works and what it can do for your company. Here, we provide a clear-eyed look at what machine learning is and how it can be used today. Machine learning is a subset of artificial intelligence that enables systems to learn and predict outcomes without explicit programming.

Machine learning is transforming business. But even as the technology advances, companies still struggle to take advantage of it, largely because they don't understand how to strategically implement machine learning in service of business goals. Hype hasn't helped, sowing confusion over what exactly machine learning is, how well it works and what it can do for your company. Here, we provide a clear-eyed look at what machine learning is and how it can be used today. Machine learning is a subset of artificial intelligence that enables systems to learn and predict outcomes without explicit programming.