1. Neural Networks: A neural network is a series of algorithms that attempt
to recognize patterns in data. Neural networks are modeled after the
structure of the human brain and consist of layers of interconnected nodes
or neurons. Each neuron receives input from the previous layer and produces
output for the next layer.
2. Deep Neural Networks: A deep neural network is a type of neural network
with multiple hidden layers. These hidden layers allow the network to learn
and extract more complex features from the input data.
3. Backpropagation: Backpropagation is an algorithm used to train deep
neural networks. It works by computing the error between the predicted
output of the network and the actual output, and then adjusting the
weights of the network to minimize
this error.
4. Convolutional Neural Networks: A convolutional neural network is a type
of deep neural network that is commonly used for image recognition tasks. It
uses a series of convolutional layers to extract features from the input
image.
5. Recurrent Neural Networks: A recurrent neural network is a type of deep
neural network that is commonly used for natural language processing tasks.
It uses a series of recurrent layers to process sequences of input
data.
6. Activation Functions: An activation function is a mathematical function
that is applied to the output of a neuron. It is used to introduce
non-linearity into the network, which allows it to learn more complex
relationships between the input and output data.
These are just a few of the basic concepts in deep learning. As the
field continues to evolve, new concepts and techniques are being developed
to improve the accuracy and efficiency of deep learning models.
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