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Deep Learning

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers.

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  • What is Deep Learning?

    Deep Learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). It excels in learning complex patterns and representations from data.

  • How does Deep Learning differ from traditional machine learning?

    Deep Learning employs neural networks with deep architectures, allowing it to automatically learn hierarchical features and representations, eliminating the need for manual feature engineering.

  • What are Neural Networks in Deep Learning?

    Neural Networks are the fundamental building blocks of Deep Learning. They consist of interconnected layers of nodes (neurons), each layer learning different features and contributing to the model's overall capability.

  • What are common applications of Deep Learning?

    Deep Learning is applied in various domains, including image and speech recognition, natural language processing, autonomous vehicles, healthcare diagnostics, and recommendation systems.

  • What is the role of Training and Backpropagation in Deep Learning?

    Training involves feeding data into a neural network, adjusting weights based on predictions, and optimizing the model. Backpropagation is the process of iteratively updating weights by minimizing the difference between predicted and actual outcomes, enhancing model accuracy.

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