Neural Networks Overview

1. Neural Networks and Economic Value

Most of the economic value generated by neural networks has come from supervised learning. In supervised learning, there's a direct mapping from an input (x) to an output (y).

2. Applications

• Housing Price Prediction
  Based on the features of a house, predict its price.

• Online Advertising
  Using user and ad data, neural networks can predict whether a user will click on an ad, leading to significant economic value.

• Computer Vision
  Input an image and classify it among 1,000 categories.

• Speech Recognition
  Convert an audio clip into a text transcript.

• Machine Translation
  Translate sentences from one language to another.

• Autonomous Driving
  Process images and radar data to locate other vehicles.

3. Types of Neural Networks

• Standard Neural Network
  Commonly used for real estate and online advertising.

• Convolutional Neural Networks (CNN)
  Suited for image data. (e.g. autonomous driving)

• Recurrent Neural Networks (RNN)
  Ideal for sequence data, such as audio or text.

4. Structured vs. Unstructured Data

• Structured Data
  Defined datasets like databases where each feature (e.g., size of a house, number of bedrooms) has a clear meaning.

• Unstructured Data
  Data like raw audio, images, or text, which historically was challenging for computers to interpret.

5. Impact on Unstructured Data

Neural networks have enabled computers to better interpret and process unstructured data, leading to applications in speech recognition, image recognition, and natural language processing.

6. Neural Network's Popularity

Despite the concepts behind neural networks being old, they have only recently started to show their potential. The reasons for this recent surge in effectiveness will be discussed in the next segment.