Newbie question about deep learning

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SUMMARY

Deep Learning is fundamentally based on Neural Networks characterized by multiple layers, which enhance their capability to learn complex patterns. The term "deep" specifically refers to the number of layers in these networks, rather than the overall size or computing power required. While increased computing power and memory are essential for implementing deep learning, they are not intrinsic to the definition of the technology. Understanding the architecture and functioning of deep neural networks is crucial for grasping the principles of deep learning.

PREREQUISITES
  • Neural Networks fundamentals
  • Understanding of multi-layer architectures
  • Basic knowledge of machine learning concepts
  • Familiarity with deep learning frameworks such as TensorFlow or PyTorch
NEXT STEPS
  • Study the architecture of Convolutional Neural Networks (CNNs)
  • Learn about Recurrent Neural Networks (RNNs) and their applications
  • Explore the use of TensorFlow for building deep learning models
  • Investigate techniques for interpreting deep learning models, such as SHAP or LIME
USEFUL FOR

Data scientists, machine learning engineers, and anyone interested in advancing their understanding of deep learning technologies and neural network architectures.

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Is Deep Learning based on Neural Networks (which seem to have been around for decades) and an increase in computing power and memory? Does it utilize very large Neural Networks?
 
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Deep learning is based on Neural Networks that have many layers in the network (see https://en.wikipedia.org/wiki/Deep_learning). The term does not refer to computing power or memory size although both are required to accomplish it. You can study a lot about the theory of deep learning without ever talking about the computer requirements. The "deep" refers to the large number of layers, not necessarily to the overall size of the network. The added layers in the second diagram below is what is meant by "deep". All those layers make it more difficult to understand how the network is arriving at its conclusions when it is working. (from https://quantdare.com/what-is-the-difference-between-deep-learning-and-machine-learning/)
1719743068883.png
 
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