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To make the most of new data science capabilities it is important to select the right tools for the task at hand.
Every project starts with an in-depth analysis of the available data sources and data quality. This helps us to give you concrete answers about how your goals can be reached.
We employ latest tools and trends in machine learning and artificial intelligence. Combined with our cloud knowledge we can implement solutions in a very short amount of time.
Covering the complete data science cycle: Definition of use-cases, data acquisition and pre-processing, model training, deployment, automation and monitoring.
This enables us to select the right tool for a task and turn ideas to working prototypes and solutions.
What is Tensor Flow
Framework for building, training, and deploying machine learning models
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. It is used for both research and production at Google.
TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015.
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What is reinforcement learning
Agent based learning in an uncertain and complex environment
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
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What is supervised learning
Connectivity, power, and IT issues solved in no time.
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).
A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
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