Artificial Neural Networks and Deep Learning
Artificial neural networks (ANNs) are statistical models directly inspired and partially modeled on neural networks (biological). They are capable of processing and modeling nonlinear dependence between inputs and outputs in parallel. They are characterized by containing flexible weights along paths between neurons that can be tuned by a learning algorithm that learns from observed data in order to improve the model.
Deep Learning is a function of artificial intelligence that imitates the workings of the human brain in processing data and creating patterns for use in decision making. It is a subdivision of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
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Artificial Neural Networks and Deep Learning Conference Speakers
Recommended Sessions
- Artificial Intelligence
- Artificial Neural Networks and Deep Learning
- Big Data to AI
- Cloud Computing and Internet of Things
- Cognitive Systems
- Computer vision and perception
- Enterprise Artificial Intelligence
- Ethics in AI
- Fuzzy logic and Fuzzy Systems
- Machine Learning
- Natural Language Processing
- Robotics and Mechatronics
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