Neural Network Hypothesis and Intuition

Neural Networks Overview

Neurons as Computational Units

At a simple level, neurons are computational units.

They:

• Take inputs (like dendrites)
• Process them
• Produce an output (like an axon)

ML as a Computational Graph

In our machine learning model:

• Inputs are features: $x_1, x_2, \dots, x_n$
• Output is the hypothesis $h_\theta(x)$
• $x_0$ is the bias unit, and it is always equal to 1

NN Types and Applications

1. Standard Neural Networks

Often used for:

• Housing price prediction
• Online advertising

2. Convolutional Neural Networks (CNNs)

Used primarily for image data

• Exploit spatial structure in images

3. Recurrent Neural Networks (RNNs)

Used for sequence data Examples:

• Audio (time series)
• Language (word-by-word sequence)

Custom Neural Networks

Tailored for specific applications Used in complex systems like autonomous driving:

• CNNs for images
• Other components for radar
• Combined into custom architectures

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