Deep Learning Concepts

Study Notes

Deep Learning

Deep learning is an advanced form of machine learning that simulates the way the human brain learns.
It creates an artificial neural network that mimics electrochemical activity in biological neurons using mathematical functions.

Artificial Neural Networks

Artificial neural networks are made up of multiple layers of neurons, defining a deeply nested function.
This architecture is the reason the technique is referred to as deep learning.
The models produced by it are often referred to as deep neural networks (DNNs).

Applications of Deep Learning

Deep neural networks can be used for many kinds of machine learning problems, including:
- Regression
- Classification
- Natural language processing
- Computer vision

Deep Learning Process

Deep learning involves fitting training data to a function that can predict a label (y) based on the value of one or more features (x).
The function (f(x)) is the outer layer of a nested function in which each layer of the neural network encapsulates functions that operate on x and the weight (w) values associated with them.
The algorithm used to train the model involves:
- Iteratively feeding the feature values (x) in the training data forward through the layers to calculate output values for ŷ.
- Validating the model to evaluate how far off the calculated ŷ values are from the known y values (which quantifies the level of error, or loss, in the model).
- Modifying the weights (w) to reduce the loss.

Training a Deep Learning Model

The trained model includes the final weight values that result in the most accurate predictions.
During the training process, the model learns the weights that will result in the most accurate predictions.

Example: Classification using Deep Learning

In a classification problem, the machine learning model must predict the most probable class to which an observation belongs.
A classification model predicts a label that consists of the probability for each class.
In the example of penguin species classification, the feature data (x) consists of four measurements: [x1, x2, x3, x4].
The label to be predicted (y) is the species of the penguin, with three possible species.
The predicted label (y) is a vector of three probability values: [P(y=0|x), P(y=1|x), P(y=2|x)].

Description

This quiz covers the fundamentals of deep learning, including artificial neural networks and their ability to simulate human brain functionality. Test your understanding of this advanced form of machine learning.