Image tagging
Image tagging is the process of automatically assigning text tags to images. Each image can have multiple tags. Use cases include photos in an e-commerce search engine, finding images quickly in a database and the diagnostic process of medical image examinations. This in turn allows reduced workload and faster classification as well as recommendation of alternative content.
In order to construct a system that can tag images, it needs a training dataset with annotated images. This can be a laborious task; depending on the complexity of the task, the training dataset needs to be large and adapted to the specific context. For example, if you need a system that can recognize french food items, it needs to be trained with foods that are common in France. For very complex tasks more data are needed. A carefully curated dataset improves the final result. Deep learning methods such as convolutional neural networks can be employed to produce a system that can automatically tag the image. Interested? I’d be happy to talk with you and show a demo. rrighart@googlemail.com
In order to construct a system that can tag images, it needs a training dataset with annotated images. This can be a laborious task; depending on the complexity of the task, the training dataset needs to be large and adapted to the specific context. For example, if you need a system that can recognize french food items, it needs to be trained with foods that are common in France. For very complex tasks more data are needed. A carefully curated dataset improves the final result. Deep learning methods such as convolutional neural networks can be employed to produce a system that can automatically tag the image. Interested? I’d be happy to talk with you and show a demo. rrighart@googlemail.com
E-commerce
E-commerce is one of the fastest growing industries. The following shows an overview of concrete case examples where data science can support significantly and improve your e-commerce.
To get insight into your e-commerce, such as customer behavior and product analysis, it is valuable to produce dashboards that can be consulted at anytime, any place. Such dashboards allow to visualize sales time-series, product tables. Other metrics of interest include customer churn rate, product affinity analyses.
Tracking time-series is crucial to decide if stock supply should be adapted. Statistical metrics can be used to detect those products that show increasing trends versus those that show a decreasing trend across a certain time window. Machine learning can be used to forecast sales volume for the future. More information? Please feel free to contact me. rrighart@googlemail.com
To get insight into your e-commerce, such as customer behavior and product analysis, it is valuable to produce dashboards that can be consulted at anytime, any place. Such dashboards allow to visualize sales time-series, product tables. Other metrics of interest include customer churn rate, product affinity analyses.
Tracking time-series is crucial to decide if stock supply should be adapted. Statistical metrics can be used to detect those products that show increasing trends versus those that show a decreasing trend across a certain time window. Machine learning can be used to forecast sales volume for the future. More information? Please feel free to contact me. rrighart@googlemail.com
Images were taken from Unsplash and credits go to the following persons:
