Coursera - Neural Networks and Deep Learning - Week 1 - Section 2

Coursera - Neural Networks and Deep Learning - Week 1 - Section 2 - Introduction to Deep Learning

2025年01月07日

Week 1: Modeling Challenges and Strategies

Section 2: Introduction to Deep Learning

1. What is a Neural Network?

Housing Price Prediction

True or false? As explained in this lecture, every input layer feature is interconnected with every hidden layer feature.

False
True

2. Supervised Learning with Neural Networks

Supervised Learning

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in the real estate application that we saw in the previous video, we use a universally standard neural network architecture, right? Maybe for real estate and online advertising might be a relatively standard neural network, like the one that we saw.
Play video starting at :3:13 and follow transcript3:13
For image applications we'll often use convolutional neural networks, often abbreviated CNN.
Play video starting at :3:21 and follow transcript3:21
And for sequence data. So for example, audio has a temporal component, right? Audio is played out over time, so audio is most naturally represented as a one-dimensional time series or as a one-dimensional temporal sequence. And so for sequence data, you often use an RNN, a recurrent neural network. Language, English and Chinese, the alphabets or the words come one at a time. So language is also most naturally represented as sequence data. And so more complex versions of RNNs are often used for these applications. And then, for more complex applications, like autonomous driving, where you have an image, that might suggest more of a CNN, convolution neural network, structure and radar info which is something quite different. You might end up with a more custom, or some more complex, hybrid neural network architecture.

Neural Network examples