### Important Deep learning Concept Explained Part – 1

- Naveen
- 0

**Neuron**

Node is a NN, typically taking in multiple input values and generating one output value by applying an activation function (nonlinear transformation) to weighted sum of input values.

**Weights**

Edges is a NN, the goal of training is to determine the optimal weight for each feature; if weight = 0, corresponding feature does not contribute.

**Neural Network**

Composed of neurons (simple building blocks that actually learn), contains activation functions that makes it possible to predict non-linear outputs.

**Activation functions**

Mathematical function that introduces non-linearity to a network e.g., RELU, tanh.

**Sigmoid function**

Function that maps very negative numbers to a number very close to 0, huge number close to 1, and 0 to 0.5. useful for predicting probabilities.

**Gradient Descent/Backpropagation**

Fundamental loss optimizer algorithms, of which the other optimizers are usually based on. Backpropagation is similar to gradient descent but for neural nets.

**Optimizer**

Operation that changes the weights and biases to reduce loss e.g. Adagrad or Adam.

**Weights/Biases**

Weights are values that the input features are multiplied by to predict an output value. Biases are the value of the output given a weight of 0.