Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton based in McLean, Virginia USA (since 2015). In those roles, he focuses on applications of data science, data analytics, data mining, machine learning, machine intelligence, and modeling across a wide variety of disciplines. He also provides leadership and mentoring to multi-disciplinary teams of scientists, modelers, and data scientists; and he consults with numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he did research, taught, and advised students in the Data Science degree programs. Before that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, including a role as NASA's Data Archive Project Scientist for the Hubble Space Telescope, contract manager in NASA's Astronomy Data Center, and program manager in NASA's Space Science Data Operations Office.

The hard truth is that many machine learning projects fail to get set into production. It takes time and real effort to move from a machine learning model to a real business application. Of course, we can't save the world with just one Hands-On tutorial, but we can at least try to make the life of a data scientist a little easier. In this blog post we will tackle these challenges by bringing the opensource world and SAP world together. In a nutshell, there will be no movement of training data from SAP HANA Cloud to our Python environment.

Originally released in 2015 as a pre-trained model for the launch of the IMDB-WIKI dataset by the Computer Vision Lab at ETH Zurich, this model is based on the VGG-16 architecture and is designed to run on cropped images of faces only. The model was then fine-tuned on the dataset for the 2015 Looking At People Age Estimation Challenge. An ensemble of these models won first place at the challenge.

Deep neural networks can perform wonderful feats, thanks to their extremely large and complicated web of parameters. But their complexity is also their curse: The inner workings of neural networks are often a mystery -- even to their creators. This is a challenge that has been troubling the artificial intelligence community since deep learning started to become popular in the early 2010s. In tandem with the expansion of deep learning in various domains and applications, there has been a growing interest in developing techniques that try to explain neural networks by examining their results and learned parameters. But these explanations are often erroneous and misleading, and they provide little guidance in fixing possible misconceptions embedded in deep learning models during training.

This article is written in response to the recent TraceTogether privacy saga. For the non-Singaporeans out there, TraceTogether is Singapore's contact tracing initiative in response to the COVID-19 pandemic in Singapore. The objective of the program was to quickly identify people who might be in close contact with anyone who has tested positive for the virus. It comprises of an app or physical token which uses Bluetooth signals to store proximity records. As at the end December 2020, 70% of Singapore residents were supposedly on the programme.

An artificial intelligence agent (AIA) goes above and beyond a trained system by utilizing the new information in the user's request to propel the neural …

Retail is a highly data driven industry. Retailers have been using traditional analytics over the years. However, the advent of Artificial Intelligence (AI) and Machine Learning (ML) has opened up a whole lot of new possibilities to gain deeper insights with data processing. The artificial intelligence in retail market is expected to grow at a CAGR of 35.9% from 2019 to 2025 to reach $15.3 billion by 2025. The growth in the artificial intelligence services in retail market is driven by several factors such as the rising number of internet users, increasing adoption of smart devices, rapid adoption of advances in technology across retail chain, and increasing adoption of the multi-channel or omnichannel retailing strategy.

IF YOU FREQUENTLY Google language-related questions, whether out of interest or need, you've probably seen an advertisement for Grammarly, an automated grammar-checker. In ubiquitous YouTube spots Grammarly touts its ability not only to fix mistakes, but to improve style and polish too. Over more than a decade it has sprawled into many applications: it can check emails, phone messages or longer texts composed in Microsoft Word and Google Docs, among other formats. Does it achieve what it purports to? But sometimes Grammarly doesn't do what it should, and sometimes it even does what it shouldn't.

Cloudera has launched Applied ML Prototypes, complete machine learning projects for use cases that give data scientists a running start on development. The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Applied ML Prototypes (AMPs) are available within Cloudera Machine Learning. By taking care of a lot of the coding and workflow grunt work, data scientists can focus on developing for the enterprise use case and iterating. Cloudera plans on adding dozens of AMP use cases that will accelerate the use of emerging machine learning.

The decision tree Algorithm belongs to the family of supervised machine learning algorithms. It can be used for both a classification problem as well as for regression problem. The goal of this algorithm is to create a model that predicts the value of a target variable, for which the decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree. It will split our data into two branches High and Normal based on cholesterol, as you can see in the above figure. Let's suppose our new patient has high cholesterol by the above split of our data we cannot say whether Drug B or Drug A will be suitable for the patient.