Keras Core Layers Dense keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer=’glorot_uniform’, bias_initializer=’zeros’, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) The dense layer can be defined as a…
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Keras Embedding keras.layers.Embedding(input_dim, output_dim, embeddings_initializer=’uniform’, embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None, mask_zero=False, input_length=None) The embedding layer is used as an initial layer in the model,…
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Kohonen Self-Organizing Maps The self-organizing maps were invented in the 1980s by Teuvo Kohonen, which are sometimes called the Kohonen maps. Since…
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Keras layers Keras encompasses a wide range of predefined layers as well as it permits you to create your own layer. It…
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Keras Locally-connected Layers LocallyConnected1D keras.layers.LocallyConnected1D(filters, kernel_size, strides=1, padding=’valid’, data_format=None, activation=None, use_bias=True, kernel_initializer=’glorot_uniform’, bias_initializer=’zeros’, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) The locally connected layer…
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Mega Case Study In this mega case study, we are going to make a hybrid deep learning model. As the name suggests,…
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Keras Merge Layers Add keras.layers.Add() This layer adds a list of inputs by taking a similar shape of the tensors list as…
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Keras Models Keras has come up with two types of in-built models; Sequential Model and an advanced Model class with functional API.…
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Recurrent Layers RNN keras.engine.base_layer.wrapped_fn() The RNN layer act as a base class for the recurrent layers. Arguments cell: It can be defined…
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Recurrent Neural Networks Why not Feedforward Networks? Feedforward networks are used to classify images. Let us understand the concept of a feedforward…
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