1. Using the MNIST dataset, create a model with four dense layers. You can use any activation function. Tune the parameters to get at least 95% accuracy. Explain your process for selecting the activation functions, loss functions, and dense layer dimensionality of the output space. Display the confusion matrix of your final model. Create a model to optimize prediction of the IRIS dataset using a perceptron. Load the IRIS dataset and split the data into two with 70% for training and 30% for testing. Create a Perceptron class and instantiate a new Perceptron. Fit the data to the model for 10 training iterations. Compute the prediction. e. Plot the prediction for 100 epochs. Generate three clusters with 500 points each with a standard normal distribution but σ1 = (1.2,0.8) µ1 = (-2,-2) with the following variance (σ) and means (µ): σ2 = (0.4, 1.3) σ3 = (0.8, 0.9) µ2 = (1,-1) µ3 = (6,12) Plot the three clusters with different colors for each to show the truth data. Select three cluster

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Math Feb 24, 2022

1. Using the MNIST dataset, create a model with four dense layers. You can use any activation function.

Tune the parameters to get at least 95% accuracy.
Explain your process for selecting the activation functions, loss functions, and dense layer dimensionality of the output space.
Display the confusion matrix of your final model.

Create a model to optimize prediction of the IRIS dataset using a perceptron.

1. Load the IRIS dataset and split the data into two with 70% for training and 30% for testing.

1. Create a Perceptron class and instantiate a new Perceptron. Fit the data to the model for 10 training iterations. Compute the prediction.

e. Plot the prediction for 100 epochs.

Generate three clusters with 500 points each with a standard normal distribution but

σ₁ = (1.2,0.8) µ₁ = (-2,-2)

with the following variance (σ) and means (µ):

σ₂ = (0.4, 1.3)

σ₃ = (0.8, 0.9)

µ₂ = (1,-1)

µ₃ = (6,12)

1. Plot the three clusters with different colors for each to show the truth data.
2. Select three cluster centers and plot the selection along with the dataset.
3. Use the k-Means clustering algorithm to assign the datapoints to a cluster. Plot each iteration and exit the process when it reaches a prior estimation error of less than 0.01.
4. Repeat the process, but use 4 clusters instead of 3.

Load the breast cancer dataset from Scikit-Learn.

1. Split the data into 50% training and 50% testing.
2. Create a SVM classifier and train the model.
3. Predict the output using the testing data.
4. What is the accuracy, precision, and recall scores?

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