Semi-supervised learning is a machine learning model that involves training a model using both labeled and unlabeled data. The idea behind semi-supervised learning is that the combination of labeled examples (where the correct output is known) and unlabeled examples can lead to improved model perfor...
Explainable AI (XAI) techniques are used after the fact to make the output of more complex ML models more comprehensible to human observers. Examples include local interpretable model-agnostic explanations (LIME), which approximate the model's behavior locally with simpler models to explain indiv...
Networks with memory are making it possible to create learning agents that can generalize to new environments, robotic arm control tasks, autonomous vehicles, time series prediction, and natural language understanding and next work prediction 5. 6 Examples of AI i...
An error function. This part of the algorithm assesses the model’s prediction. If there are examples that have already been investigated, an error function can create a comparison to evaluate the accuracy of the model. A model optimization process. If the model can adjust more easily to the ...
learning tasks, where the goal is to learn underlying patterns in the data without needing labeled examples. They can also be used for semi-supervised or supervised tasks where the model learns a representation of the normal data and then uses it to detect anomalies. Some examples of these ...
Fairly robust documentation with a lot of up-to-date examples. Both code and concepts are well described. DevOps operability After the deployment, the DevOps teams are usually responsible for monitoring and maintaining the production applications. The model serving tools need to be accessible to De...
Famous examples includeGoogle's TensorFlow, theopen-source library Keras, thePython library scikit-learn, thedeep-learning framework CAFFEand themachine-learning library Torch. Further reading Special report: Harnessing IoT in the enterprise (free PDF)(TechRepublic) ...
Supervised learningalgorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corres...
With error determination, an error function is able to assess how accurate the model is. The error function makes a comparison with known examples and it can thus judge whether the algorithms are coming up with the right patterns. Model optimization process ...
介绍:主讲人是陶哲轩,资料Probability: Theory and Examples,笔记. 《Data Science Learning Resources》 介绍:数据科学(学习)资源列表. 《8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset》 介绍:应对非均衡数据集分类问题的八大策略. ...