Neural network vs Deep learning

Hey everyone, Artificial intelligence (AI) is a vast and fascinating field, and lately I’ve been diving into the world of neural networks and deep learning. They both seem to be involved in making computers “think” and learn, but the terminology gets confusing! Are neural networks and deep learning the same thing? Or is deep learning some kind of super-powered version of a neural network? Hoping to untangle this web of terms. Any AI enthusiasts out there who can explain the difference between neural networks and deep learning in a simple way?

Thanks

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Imagine your brain as a complicated learning system. Neural networks are essentially little, simpler copies of that system. They can use data to make judgements, but on a much smaller scale. Deep learning is like giving those neural networks more friends to collaborate with. With additional assistance, kids may tackle even more difficult challenges, such as understanding spoken language or recognising faces in photographs. So, deep learning is a more powerful type of neural network.

Hi Oliver… Neural networks and deep learning are distinct yet interconnected concepts in artificial intelligence. Neural networks mimic the structure and function of biological neurons, consisting of interconnected nodes arranged in layers. They process information through these layers, adjusting weights based on training data to improve accuracy in tasks like image recognition or language processing. Deep learning, on the other hand, refers to the specific technique of using neural networks with multiple layers (deep neural networks) to automatically learn patterns and features from raw data. This approach has revolutionized AI by enabling systems to autonomously extract complex representations from large datasets, leading to breakthroughs in areas such as computer vision, natural language understanding, and robotics. Thus, while neural networks form the foundation, deep learning represents a powerful subset leveraging deep architectures to achieve advanced learning capabilities directly from data.

I’d also like a brief summary. From what I gather, it was once tough to train deep neural networks. But a few breakthroughs helped, like using autoencoders for initial weight setup in unsupervised pre-training.