Deep learning algorithms are used to create neural networks. These algorithms are responsible for learning the parameters of the neural network, such as the weights and biases.
Backpropagation is a method used to train neural networks. It involves propagating the error
from the output layer back through the hidden layers in order to adjust the weights and biases.
Deep learning is a powerful tool for building sophisticated machine learning models. However, it can be difficult to get started with deep learning, due to the complexity of the algorithms and the need for specialized hardware.
There are many different deep learning frameworks available, each with its own advantages and disadvantages. The most popular deep learning frameworks are TensorFlow, Keras, PyTorch, Caffe, and
MXNet.
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