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Getting familiar with Machine Learning | Neural networks vocabulary Neural networks always performs well with training. To perform training on the neural network we need data. Following is the example data set of iris flowers. It has 5 columns. Sepel length, Sepel width , Petal Length , Petal Width decides the species of the iris flowers. We are going to predict the species of a particular flower depending on these characteristics. According to that knowledge we have 4 characteristics of data which we know. We call them features. So Sepel length, Sepel width , Petal Length , Petal Width are the features of this data set. Species field is the label which we are going to predict. Rows of this data set are called examples . Which means , example is a set of features and labels for a particular record. We can optimize the neural network model by training. When we train the model by using the examples with labels we call...
Keras Sequential Model Creating the model | Specifying input size and batch size | specifying the activation function Sequential Model : Linear stack of layers. Create a Sequential Model by passing the layer instances to the constructor. Import sequential model from Keras models. from keras.models import Sequential Import dense layer and activation function from keras layers from keras.layers import Dense, Activation Create sequential model Input = can be of any dimension or a single element Shape = length along each dimension of the input Model needs to know the input shape it expects. Only the first layer of the sequential model should know its input shape. Batch = collection of inputs Batch Size = no of inputs Create the model model = Sequential() Create the layer with batch size of 32 and input size of 784 Dense(32,input_dim=784) Add the la...

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