In Tymon Global We Provide Solutions And Applications Of Machine Learning For Companies And Businesses

Machine Learning Applications For Companies

Before starting to develop the information of this branch of the AI ​​on our website, we consider how to approach it for understanding without complexity, because what interests us most is to solve your doubts and open yourself to a world of possibilities.

The Machine Learning represents an approach to operating mode of the human nervous system. Our brain has a complex structure, in which nuclei and differentiated areas whose neural networks are specialized to perform specific tasks have been discovered. Thus, for example, we know that there are specific language centers or that there are specialized networks to detect different aspects of the vision and even areas closely related to the recognition of faces and their emotional expression.

Some work areas are:

– Image recognition: To improve occupational safety, supervise spaces (hospitals, business centers, etc.), quality controls, patient diagnosis, detection of defective items, etc.

– Robots and drones totally autonomous.

– Analysis of user emotions: from their communications and images.

– Detection of problems: from large amounts of information such as images, customer emails, etc.

– Creation of AI Brain : Creation of virtual assistants that can adapt their behavior based on their environment or the actions of other users. Applicable to games or user support systems.

Hence the idea of ​​creating artificial neural networks, to perform, tasks as complicated as the recognition of images or the interpretation of natural language. An artificial neuron network is a mathematical tool that models, in a simplified way, the functioning of neurons in the brain. The architecture is as follows: the neurons are organized in layers, where the green neurons are the inputs that receive the information, the blue ones are the hidden ones, which contain intermediate calculations of the network, and the yellow ones are the outputs that contain the result. Normally there is an input layer, an output layer and several hidden ones. The more hidden layers, the more complex the network is and the better results it predicts, but it is also more difficult to create the model.