A dashboard is an interactive visualisation of certain data (e.g. business figures) on a separate user interface. The user can change the time period of the data displayed or zoom into a chart to look at something in more detail. Dashboards can be used to transform data into information from which knowledge can be generated.
Data mining refers to the computer-aided evaluation and analysis of large volumes of data to identify patterns and correlations. It uses automated processes for pattern recognition, methods from artificial intelligence, statistics and data analysis.
Companys and organisations can use data mining to gain valuable knowledge to help them make better decisions. It involves using historical data to predict likely future developments and identify possible trends or anomalies.
One example is the analysis of customer data in an online shop. By analysing purchasing behaviour, search queries and demographic information, targeted marketing can be developed.
Data mining is also used in the context of automatic text generation. Text mining uses techniques such as natural language processing (NLP) and machine learning (ML) to identify patterns in text. By using algorithms and machine learning, it is possible to generate new coherent and meaningful sentences or paragraphs. This can be used to automatically generate articles or stories.
Deep learning (DL) is a branch of artificial intelligence and a machine learning (ML) technique based on artificial neural networks. Deep learning algorithms use multiple layers (hence the name) to process and analyse information. This can be used for tasks such as image and speech recognition and natural language processing. In other words, for processes that humans perform intuitively and that cannot be calculated using formulae. The necessary complexity is achieved using a digital layer model. In DL complex learning effects – and decisions based on them – are based on the combination of many small, simple decisions and learning effects.
Deep learning has greatly improved the results of machine learning in many areas, but it is also much more resource-intensive than non-deep ML methods, i.e. single-layer neural networks, or other algorithms that do not require neural networks at all. (more…)
Defensive distillation is an adversarial training technique used in the field of machine learning, specifically in the context of deep learning. The technique protects neural networks from adversarial attacks and makes an algorithm’s classification process more flexible so the model is less vulnerable to exploitation. (more…)