Milan Dordevic MBA, PMP is a certified project management expert, author, speaker, business and technology mentor for high-tech. Artificial intelligence (AI) is reimagining the business world, boosting innovation and productivity, and helping organizations think bigger. Organizations can use AI to improve their products, processes and decision-making. Using the technology available today, organizations should be able to achieve organizational agility powered by AI. Organizational leaders need to continuously drive change and evaluate which areas, and at what complexity, AI should be utilized to support company goals and further growth.

An autoencoder is a type of neural network that is comprised of two functions: an encoder that projects data from high to low dimensionality, and a decoder that projects data from low to high dimensionality. To understand how these two functions work, let's consider the following images: While the encoder reduces the dimensionality of input data, the decoder projects samples from low dimensionality back to higher dimensionality. For example, if one constructs a decoder that projects data from 2 dimensions to 748 dimensions, it becomes possible to project arbitrary positions in a two dimensional plane into a 748 pixel image. Click around in the figure below to see how a decoder projects from 2 to 748 dimensions. An autoencoder is a neural network that combines the encoder and decoder discussed above into a single model that projects input data to a lower-dimensional embedding (the encode step), and then projects that lower-dimensional data back to a high dimensional embedding (the decode step).

This project is based on a regression problem. The task is to train a machine learning model that can predict credit card spending based on historical data. This model can help banking industries to decide credit card limit based on user's past experience. This project has 15 columns to find the best features out of them. In this way, you will also learn different features elimination and selection techniques.

Different research institutions use research information for different purposes. Data analyses and reports based on current research information systems (CRIS) provide information about the research activities and their results. As a rule, management and controlling utilize the research information from the CRIS for reporting. For example, trend analysis helps with business strategy decisions or rapid ad-hoc analysis to respond effectively to short-term moves. Ultimately, the analysis results and the resulting interpretations and decisions depend directly on the quality of the data.

Three applications illustrate how companies are beginning to embrace data-driven solutions while establishing a foundation for future initiatives. The construction business faces a major productivity challenge. While labor productivity in the global economy has increased by an average of 2.8 percent a year over the past two decades, and in manufacturing by an impressive 3.6 percent, the construction sector has registered a mere 1 percent annual improvement. As the capital-project partners responsible for execution, engineering and construction (E&C) firms are well positioned to drive changes that can help close this troubling gap. To do so, some are turning to data-driven solutions that have already revolutionized many other corners of the economy.

The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects e.g., customers' needs, developers' performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.

The Top Gun pilots of the future may use radical virtual instruments instead of the traditional dials and levers. The radical system will monitor a pilot's every move, tracking their gaze and brainwaves to work out exactly what they are looking at - and predicting what they want to do next. Experts at BAE Systems say their'mindreading' technology will enable pilots to control the fighter jet of the future with the blink of an eye. The radical system will monitor a pilot's every move, tracking their gaze to work out exactly what they are looking at - and predicting what they want to do next The system would use the same augmented reality technologies being developed by tech firms to create consumer glasses that can project data on top of the real world, with Apple, Google and others all hard at work on systems. The radical system will monitor a pilot's every move, tracking their gaze and brainwaves to work out exactly what they are looking at - and predicting what they want to do next.

Designing a building, developing a constructible model from a design or working out how to go about constructing a complicated model are all tasks that already contain some degree of automation. So when researchers and others in the architectural, engineering and construction world start talking about bringing artificial intelligence into the mix, many say it's already here. But recent advances in generative design, safety analysis and 5D scheduling are only the first hints of what sophisticated algorithms and deep-learning AI can bring to construction. Getting smart algorithms and other AI-derived technologies onto the project team may not be as far-fetched an idea as it once was. But rather than having a computer that takes over the existing job duties of an architect or engineer, those professions may soon have some form of AI-based assistant offering options and providing clarifications all along the way.

This is a TensorFlow implement of face age and gender estimation which first using dlib to detect and align faces in the picture and then using a deep CNN to estimate age and gender.As you can see below,this project can estimate more than one face in a picture at one time. This project has following dependencies and tested under CentOS7 with Python2.7.14 At present,our deep CNN uses FaceNet architecture,which based on inception-resnet-v1 to extract features.To speed up training,we use the pretrained model's weight from this project and have converted the weight to adapt our model,you can download this converted pretrained facenet weight checkpoint from here or here.Extract it to path models. NOTE: This step is optional,you can also train your model from scratch. NOTE: Using the flag --cuda will train the model with GPU.

Initial open source software (OSS) projects rely on large repositories for hosting and distribution until they become independent. A huge amount of project metadata is collected and maintained in such software repositories providing useful information about projects and their success. In this paper we propose a data mining approach that processes the metadata contained in such OSS repositories. The proposed approach aims at the construction of a classifier that is trained on the metadata of existing projects and predicts the successful continuation of any given OSS. The successfulness of a project is defined with regard to the confidence level of the classifier which predicts that this project will be ported in widely used OSS projects (e.g.