What is dAIve?
dAIve engineering is a no-code Machine Learning tool for instant predictions and data insights.
By using existing data like test data, simulation data, results of physical experiments or other historic data, you'll be able to train your tailored model within minutes to be able to do instant predictions.
The analysing capabilities of dAIve will help you to learn more about your data, identify data gaps and identify relations between different parameters of your data set.
What is Machine Learning?
Machine learning is a subfield of artificial intelligence that focuses on creating algorithms that can automatically learn and improve from data. In other words, it is the process of teaching computers to learn from data, without being explicitly programmed.
Machine learning algorithms can be used to solve a wide range of problems, such as image recognition, natural language processing, fraud detection, recommendation systems, and many others. These algorithms use statistical models to find patterns and relationships in large datasets, and then use these patterns to make predictions or decisions.
There are several types of machine learning algorithms, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each of these algorithms uses a different approach to learning from data, depending on the type and amount of data available and the nature of the problem being solved.
Overall, machine learning has become an essential tool for businesses and organizations across a wide range of industries, allowing them to extract valuable insights from their data and make more informed decisions.
What kind of Machine Learning algorithms does dAIve use?
dAIve will have serveral modules we will launch one after another. At the moment dAIve engineering is available, which works with Multi-Layer Perceptron (MLP).
MLP is a type of artificial neural network that is widely used in machine learning. An artificial neural network is a set of interconnected nodes or neurons that are designed to simulate the behavior of the human brain.
In an MLP, the neurons are organized into layers, with each layer consisting of a set of neurons that are connected to the neurons in the previous layer. The first layer is known as the input layer, which receives the data to be processed. The output layer produces the final output of the network, while one or more hidden layers perform computations on the data between the input and output layers.
Each neuron in an MLP computes a weighted sum of its inputs and applies an activation function to produce an output. The weights and biases of the neurons are learned during training, using a process called backpropagation, which adjusts the weights and biases to minimize the difference between the predicted output and the actual output.
MLPs are used for a wide range of tasks, including classification, regression, and pattern recognition. They are particularly useful for tasks that involve complex, nonlinear relationships between the input and output variables.
How does dAIve work?
dAIve is a user-oriented tool that allows users to train neural networks without programming knowledge or in-depth machine learning expertise. dAIve guides the user step-by-step through the activities necessary to train a model with existing data, which can then be used to make predictions for new constellations of input parameters. This data can come from physical experiments, simulations, sensors or other historical data sources.
How do I know if my training data is sufficient?
dAIve not only supports the training and prediction process, it also helps to analyze the existing training data. During training, the existing data is checked and possible data gaps can already be identified via the prediction quality.