Deep Learning is a a sub-field of Machine Learning, using Deep Neural Networks(DNN). the word “Deep” implies refers to the depth of layers in a Artificial Neural Networks (ANN).

Deep Learning is better than traditional machine learning when we have high dimensional data; where we have large inputs and outputs. Deep Learning is the best solution when we try to mimics the human cognition.

Tip

Deep Neural Networks are capable of Feature learning.

Concepts:

- Input layer, Hidden Layer, and Output layer:
- The first layer is the input layer. Each node in this layer takes an input and then passes its output as the input to each node in the next layer.
- Middle layers are called hidden layer.
- Last layers are output nodes.

- Backpropagation(backward propagation of errors): This helps to calculate the Gradient of Loss Functions with respect to all the weights in the network. Backward propagation of errors helps to calculate the gradient of a loss function with respect to all the weights in the network.
- Chain Rule: The chain rule is a way to compute the derivative of a function whose variables are themselves functions of other variables.
- Activation Functions
- Weights Initialization

Concerns in building a Deep Learning Model:

- Performing Experiments
- Making changes to the model
- Keeping track of changes and results

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