Deep Neural Nets (Deep Learning) is a type of machine learning that is inspired by the structure and function of the brain. Deep Neural Nets (Deep Learning) algorithms are able to learn from data, without being explicitly programmed.
Deep Neural Nets (Deep Learning) is a subset of machine learning and is also sometimes referred to as deep learning.
Some of the tasks of Deep Neural Nets (Deep Learning)
Entity recognition. Entity recognition is the task of identifying named entities in text, such as people, places, organizations, and products.
Machine translation. Machine translation is the task of translating text from one language to another.
Part-of-speech-tagging. Part-of-speech tagging is the task of assigning parts of speech to each word in a sentence. Deep Neural Nets (Deep Learning) can be used for part-of-speech-tagging tasks.
Question answering. Question answering is the task of answering questions posed in natural language. Deep Neural Nets (Deep Learning) can be used for question answering tasks.
Who uses Deep Neural Nets (Deep Learning)
Some examples of who uses Deep Neural Nets (Deep Learning) include:
- Baidu
- Amazon
- DeepMind Technologies
What types of data can be used with Deep Neural Nets (Deep Learning)?
Some examples of data that can be used with Deep Neural Nets (Deep Learning) include:
- Text data
- Image data
- Audio data
- Video data
Benefits of using Deep Neural Nets (Deep Learning)
Some benefits of using Deep Neural Nets (Deep Learning) include:
Increased accuracy. Deep Neural Nets (Deep Learning) can achieve high levels of accuracy.
Automatic feature engineering. Deep Neural Nets (Deep Learning) can automatically extract features from data.
End-to-end learning. Deep Neural Nets (Deep Learning) can learn tasks directly from data, without the need for manual feature engineering.
Scalability. Deep Neural Nets (Deep Learning) can be scaled to large datasets.
What are some challenges of using Deep Neural Nets (Deep Learning)?
Some challenges of using Deep Neural Nets (Deep Learning) include:
- Large amounts of data are required in order for the algorithm to learn
- It can be time-consuming to train the algorithm
- It can be difficult to interpret the results