From speech recognition to diverse predictions, deep learning works on a constant learning in your neural network, serving from parameters to independently recognize varied patterns. Its use brings many facilities in our daily lives, so it is important to understand more about its characteristics. Learn more about it in the following lines.
As a subfield of machine learning, deep learning is designed to simulate a human brain using algorithms. Its objective is to learn and optimize concepts and predictions from a large amount of information and examples.
Everything is connected by multi-layer neural networks with a clear focus on being as close to organic learning as possible. Its use can be directed to many forms of artificial intelligence, from applications to services.
With everything working, training occurs frequently, always processing new data from different sources. One of the most interesting points is that since the analysis of information takes place in real time, deep learning no longer needs any human intervention for adjustments or the like.
Without really noticing it, we live with the use of this technology on a daily basis.
Applications in everyday life
If we think of something that is constantly learning, always changing, adapting and practically thinking on its own, then it is not very difficult to find uses for this technology.
By using a chatbot in a streaming session, the user gains a strong ally against spam or people with inappropriate language. This is one of the uses of a virtual assistant.
Through items such as speech recognition and real-time language processing, the technology allows the computer or device to hear, read and respond appropriately to the moment. Always learning and improving. Alexa, Siri, and Cortana are great examples.
Focusing on improving the experience for its users, social networks make use of learning algorithms all the time. From there, it is possible to prevent cyberbullying and delete inappropriate messages, for example.
Its use can also be focused to recommend perfect pages, products and even digital influencers for each individual.
Still unpopular, many companies like Google and Nvdia see self-driving cars as a sure future. Vehicles that will drive without a person at the wheel have been part of fiction for a long time, but they seem increasingly closer to everyday reality. In San Francisco, for example, it is already possible to find this technology in the most touristy parts of the city.
The artificial intelligence behind the steering wheel uses technologies such as GPS to follow the path, as well as various analyzes to define distance, speed and even braking responses.
Diferença entre deep learning e machine learning
Even though it is a subfield of artificial intelligence technology, there are details between the two points mentioned. First of all, it is extremely important to understand that all deep learning is machine learning, but it works with small differences and processes.
The main thing is to understand that machine learning, however advanced it may be, still needs human intervention at some point. An AI can improve and develop over time, but if you encounter any errors or difficulties, a person will need to make the necessary adjustments.
In deep learning, the goal is that this is no longer necessary. The “machine” itself, so to speak, can predict possible errors and problems, adjusting and adapting to fix them. All from the algorithms used.
At the end of the day, the focus is on giving the clearest feeling that AI really has its own brain.
Now you know that whenever you ask Alexa for something or ask funny questions, you are dealing directly with this technology. Can you tell us other interesting applications of this in our daily lives? Don’t forget to join the community technoblog!
With information: IBM.