Deep learning is a branch of artificial intelligence (AI) which enables machines to learn and make decisions on their own by processing large amounts of data. Unlike traditional machine learning, deep learning involves using neural networks with multiple layers adept at data handling and extraction of features.
How does it work?
In deep learning, neural networks are used to imitate the way human brains work by processing data through multiple layers, and each layer is potentially capable of extracting one or more specific features from the input. The multiple layers enables the algorithm to recognize complex patterns in the input data.
Applications of deep learning
1. Image recognition: With deep learning, machines can easily recognize images and objects and classify them accordingly. This ability allows industries such as healthcare, automotive and manufacturing to use technology, such as robots to perform tasks like sorting and identifying defective products.
2. Natural Language Processing: Deep learning is also instrumental when it comes to natural language processing (NLP). This makes it necessary for machines to understand human language and recognize speech and handwriting.
3. Healthcare: Deep learning algorithms have applications in fields like drug discovery, patient care, and diagnosis. This helps the healthcare industry to improve the accuracy and efficiency of diagnoses and treatment procedures.
4. Automotive Industry: Companies in the industry are using deep learning to improve driving safety. Self-driving cars can now recognize and avoid obstacles on the road, making driving safer.
5. Video Surveillance: Deep learning is also being used for facial recognition, which allows for tracking of suspected criminals and people of interest to authorities.
In conclusion, deep learning allows machines to learn and make decisions like humans while continually increasing in accuracy as more data is gathered and processed. It is worth noting that this branch of AI is still in its infancy, and further development is expected in years to come.
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