Ganttic is a flexible drag-and-drop scheduler for resource planning. Its resource-centric Gantt charts provide a holistic view of your equipment, personnel, facilities, and vehicles, providing a clear understanding of who or what is engaged and when.
Beyond its scheduling capabilities, Ganttic enables a deeper level of resource management and project portfolio oversight. Harness the power to optimize resource utilization, generate detailed reports, and establish project or resource-breakdown structures that streamline the planning process.
Unlimited Custom Views help segment large resource pools, giving different managers the power to organize their teams and departments according to their own needs. Create unique data fields to incorporate data that matters, and ensuring the right resource is booked for the job. Easily share Views to facilitate collaboration among teams and stakeholders, while notifications, calendar syncs, and a mobile app keep the right individuals informed of any changes. With unlimited user access in all subscriptions, everyone stays up to date.
Take advantage of a free 14 day trial with complimentary training and onboarding from our dedicated support team.
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In just a few days, you can integrate and customize a lightning-fast financial table with your product. You can make changes or create a completely new interface. You want more? We offer a full access alternative. Data feeds with futures and indices, equities, FX and cryptocurrencies by default. Sign up now to get your data feeds. DXcharts can be integrated with any market data source, as it is data feed-agnostic. Native libraries for all platforms. Native web, native mobile & desktop. Get a solution that is specifically tailored to your product. Analyzing statistics from trading activity can help you evaluate securities and predict their future movements. You can create custom studies with the intuitive dxScript. You can adjust the layout of charts however you like and sync them by instrument, chart type and timeframe, range, studies & appearance.
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TensorFlow
TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.
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Keras
Keras is an API tailored for human users rather than machines. It adheres to optimal practices for alleviating cognitive strain by providing consistent and straightforward APIs, reducing the number of necessary actions for typical tasks, and delivering clear and actionable error messages. Additionally, it boasts comprehensive documentation alongside developer guides. Keras is recognized as the most utilized deep learning framework among the top five winning teams on Kaggle, showcasing its popularity and effectiveness. By simplifying the process of conducting new experiments, Keras enables users to implement more innovative ideas at a quicker pace than their competitors, which is a crucial advantage for success. Built upon TensorFlow 2.0, Keras serves as a robust framework capable of scaling across large GPU clusters or entire TPU pods with ease. Utilizing the full deployment potential of the TensorFlow platform is not just feasible; it is remarkably straightforward. You have the ability to export Keras models to JavaScript for direct browser execution, transform them to TF Lite for use on iOS, Android, and embedded devices, and seamlessly serve Keras models through a web API. This versatility makes Keras an invaluable tool for developers looking to maximize their machine learning capabilities.
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