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.

The age of artificial intelligence (AI) has arrived, changing the world around us in exciting and unpredictable ways. We are getting accustomed to AI and our children will be highly dependent on it. AI helps bring about new careers, discover new drugs, augment our senses, and influence both our interaction with the world and our understanding of it. One day, it may help us eradicate war, disease, and poverty. Unfortunately, we cannot fully predict the effects of AI on society.

Light Gradient Boosted Machine, or LightGBM for short, is an efficient and effective implementation of the gradient boosting algorithm. LightGBM extends the gradient boosting algorithm by adding a type of automatic feature selection as well as focusing on boosting examples with larger gradients. This can result in a dramatic speedup of training and improved predictive performance. As such, LightGBM has become a de facto algorithm for machine learning competitions when working with tabular data for regression and classification predictive modeling tasks. As such, it owns a share of the blame for the increased popularity and wider adoption of gradient boosting methods in general, along with Extreme Gradient Boosting (XGBoost).

It can't be bargained with. It can't be reasoned with. It doesn't feel pity, or remorse, or fear. And it absolutely will not stop, ever, until you have clicked on Add to Basket. T-800, eat your endoskeleton lubricant pump out: I'm talking about the new wave of e-commerce virtual assistants. It seems that you are not the only one to have noticed that responding to the "Do you need help?" popup while shopping online was about as productive as telling Eliza about your mother in 1992.

Researchers have developed an innovative animal-borne data-collection system assisted by artificial intelligence to track previously unobserved behaviors in wild animals. The method uses low-cost sensors to automatically detect and record behaviors of specific interest. The new system greatly outperformed previous random sampling methods in capturing the target behavior and the researchers were able to observe previously unreported foraging behaviors in gulls. These findings can be applied to support further data collection in the wild. Bio-logging is a technique involving the mounting of small lightweight video cameras and/or other data-gathering devices onto the bodies of wild animals.

Sridhar Seshadri is the Founder of couple of Deep Technology Startups. In his previous corporate life as Director of Technology & Product at CognitiveScale Inc, a 200 Million Dollar company funded by Intel Capital, Microsoft Ventures, IBM, and Norwest Capita he led AI product development for global market in verticals of Health Care, Retail & Commerce, Financial Services. Sridhar is an BITS, Pilani Alumnus and has worked for companies like Facebook, and EA Sports and led the Product Insights and Predictive Analytics function and was the recipient of The CEO's EA Spotlight Award For Driving Innovation. His experience covers developing Technology, Management, and Products development leveraging Machine Learning, NLP, Social, Mobile, & Cloud.

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.

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 Setup a virtual environment and activate it.