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Is behind a self-driving car that is capable of processing visual data on road markings, traffic signs and various other objects on the road in real-time? Well, one of the technologies behind self-driving cars is deep learning. Deep learning is a subfield of machine learning whose. Algorithms are inspired by the structure of the human brain. Currently. Deep learning techniques are very popular among data practitioners. And attract the attention of many parties. This is because deep learning technology has been applied in various . High-tech products such as self-driving cars. Additionally, it is also behind the products and services we use every day. Examples include digital assistants, Google Translate . And voice-activated devices (smart devices that can be activated by voice.

Interesting right So for those

historical data or time series. It is a complex deep learning algorithm and can learn long-term information very well. LSTM is very powerful for solving various complex problems such as speech recognition, speech to text applications, music composition, and developments in the pharmaceutical sector. Self Organizing Maps (SOM) The whatsapp number list final type is self organizing maps or SOM. This algorithm is able to create data visualizations independently. SOM was created to help users understand high-dimensional data and information. Benefits of deep learning After listening to the explanation above, let’s discuss the benefits of deep learning. Here are some of the benefits of implementing it: Can process unstructured data such as text and images. Can automate the feature extraction process without

This type of Deep learning

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Convolutional Neural Network (CNN) CNN consists of many layers to process and extract features from data. It is usually used to process images and detect objects. Currently, CNN is widely used to identify satellite images, medical images, and detect anomalies. Recurrent Neural Network (RNN) Recurrent Neural Networks (RNN) is a form of Artificial Neura Calling List lNetworks (ANN) architecture that is specifically designed to process sequential data. RNN is usually used to solve historical or time series data problems, for example weather forecast data. Apart from that, RNN can also be implemented in the field of natural language understanding, for example language translation. Long Short Term Memory Network.

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