Programming for Data Science, Lec 15: Neural Networks, TensorFlow AutoDiff, and Keras
#datascience #machinelearning #neuralnetworks
Neural networks are computational models inspired by the human brain's structure and function, designed to recognize patterns and solve complex problems in various domains.
Unlike simpler linear models, which directly map input features to outputs, neural networks introduce one or more hidden layers between inputs and outputs, providing much greater expressive power.
In Keras, a `Dense` layer is a fully connected neural network layer where each input node is connected to each output node. The Dense layer is a core layer in Keras and is used frequently in creating neural network models. The basic syntax for adding a Dense layer is as follows:
```
from keras.layers import Dense
Adding a Dense layer
dense_layer = Dense(units, activation=None, ...)
```
units: This is a required parameter and represents the number of neurons (output size) in the layer.
activation: The activation function to use. If you don't specify anything, no activation is applied (i.e., "linear" activation: a(x) = x). Common activation functions include 'relu', 'elu', and 'sigmoid'.