FUNDAMENTALS OF DEEP LEARNING (Francesca Maschio & Matia Polimeno)

FUNDAMENTALS OF DEEP LEARNING

work of: Francesca Maschio & Mattia Polimeno

DEEP LEARNING

is based on artificial neural networks (ANNs)

these are networks of simple nodes, or neurons, that are interconnected and can learn to recognize patterns of input

ANNs are similar to the brain in that they are composed of many interconnected processing nodes, or neurons.

Each node is connected to several other nodes and has a weight that determines the strength of the connection.

LAYER-WISE

FIRST LAYER OF NEURALNETWORK extracts low-level features from the data (such as edges and shapes)

SECOND LAYER combines these features into more complex patterns

and so on until the FINAL LAYER (the output layer) produces the desired result

Each successive layer extracts more complex features from the previous one until the final output is produced

this process is also known as...

FORWARD PROPAGATION

can be used to calculate the outputs of deep neural networks for given inputs

It can also be used to train a neural network by back-propagating errors from known outputs.

BACKPROPAGATION

This process of forward and backward propagation is repeated until the error is minimized and the network has learned the desired pattern.

is an important part of deep learning because it allows for complex models to be trained quickly and accurately

works by comparing the network's output with the correct output and then adjusting the weights in the network accordingly

is a supervised learning algorithm, which means it requires a dataset with known correct outputs

...let's look at some types of deep learning models and neural networks:

RECURRENT NEURAL NETWORKS (RNN)

CONVOLUTIONAL NEURAL NETWORKS (CNN)

LONG SHORT-TERM MEMORY (LSTM)

CONVOLUTIONAL NEURAL NETWORKS (CNN)

Lare commonly used to analyze visual content. They are similar to regular neural networks, but they have an extra layer of processing that helps them to better identify patterns in images. This makes them particularly well suited to tasks such as image recognition and classification.

RECURRENT NEURAL NETWORKS (RNN)

is a type of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior.

LONG SHORT-TERM MEMORY (LSTM)

are a type of recurrent neural network that can learn and remember long-term dependencies. They are often used in applications such as natural language processing and time series prediction.

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