Assignment : keras code for a multilayer perceptron neural network To type in Jupyter notebook (ipynb file). In the following 4 questions the code should simply build the architecture of the model....

This homework (attached) needs to be done in Jupyter notebooks (ipynb file).This hw is on machine learning with a focus on keras code for a multilayer perceptron neural network.In the 4 questions attached, the code should simply build the architecture of the model.You will not need to not run it on real data.My budget for this is $110.
My deadline for this is Thursday 22nd of April, 5pm, EST.


Assignment : keras code for a multilayer perceptron neural network To type in Jupyter notebook (ipynb file). In the following 4 questions the code should simply build the architecture of the model. You will not need to not run it on real data. 1. Write the keras code for a multilayer perceptron neural network with the following structure: Three hidden layers. 50 hidden units in the first hidden layer, 100 in the second, and 150 in the third. Activate all hidden layers with relu. The output layer should be built to classify to five categories. Further, your optimization technique should be stochastic gradient descent. (This code should simply build the architecture of the model. You will not run it on real data.) 2. Write the keras code for a multilayer perceptron neural network with the following structure: Two hidden layers. 75 hidden units in the first hidden layer and 150 in the second. Activate all hidden layers with relu. The output layer should be built to classify a binary dependent variable. Further, your optimization technique should be stochastic gradient descent. (This code should simply build the architecture of the model. You will not run it on real data.) 3. Write the keras code for a convolutional neural network with the following structure: Two convolutional layers. 16 filters in the first layer and 28 in the second. Activate all convolutional layers with relu. Use max pooling after each convolutional layer with a 2 by 2 filter. The output layer should be built to classify to ten categories. Further, your optimization technique should be stochastic gradient descent. (This code should simply build the architecture of the model. You will not run it on real data.) 4. Write the keras code for a convolutional neural network with the following structure: Two convolutional layers. 32 filters in the first layer and 32 in the second. Activate all convolutional layers with relu. Use max pooling after each convolutional layer with a 2 by 2 filter. Add two fully connected layers with 128 hidden units in each layer and relu activations. The output layer should be built to classify to six categories. Further, your optimization technique should be stochastic gradient descent. (This code should simply build the architecture of the model. You will not run it on real data.)