Neural Network Representations (C1W3L02) | Summary and Q&A

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August 25, 2017
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DeepLearningAI
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Neural Network Representations (C1W3L02)

TL;DR

Learn about the structure of a neural network and the meaning behind hidden layers and activations.

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Key Insights

  • 🔠 The structure of a neural network consists of input, hidden, and output layers.
  • 😫 The hidden layer is called "hidden" because its values are not observed in the training set.
  • 🧭 Activations are the values passed on between different layers of the neural network.

Transcript

see me draw a few pictures of your neural network in this video we'll talk about exactly what those pictures means in other words actly what those little neural networks have been drawing on represent and we'll starts were focusing on the case of neural networks with what's called a single hidden layer she is a picture of a neural net let's give di... Read More

Questions & Answers

Q: What are the three layers in a neural network?

The three layers in a neural network are the input layer, hidden layer, and output layer.

Q: Why is the hidden layer called "hidden"?

The hidden layer is called "hidden" because its values are not observed in the training set.

Q: What does the term "activations" refer to in a neural network?

Activations refer to the values that different layers of the neural network pass on to the subsequent layers.

Q: How is a two-layer neural network different from other neural networks?

A two-layer neural network refers to a network with one hidden layer, while other neural networks may have more hidden layers.

Summary & Key Takeaways

  • This video explains the structure of a neural network, focusing on a single hidden layer.

  • There are three layers in a neural network: input layer, hidden layer, and output layer.

  • The hidden layer is called "hidden" because its values are not observed in the training set.

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