Fill This Form To Receive Instant Help

Help in Homework
trustpilot ratings
google ratings


Homework answers / question archive / 1

1

Math

1. Using the MNIST dataset, create a model with four dense layers. You can use any activation function.
  1. Tune the parameters to get at least 95% accuracy.
  2. Explain your process for selecting the activation functions, loss functions, and dense layer dimensionality of the output space.
  3. Display the confusion matrix of your final model.
  1.  

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.

  1.  

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.
  1.  

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?

Option 1

Low Cost Option
Download this past answer in few clicks

23.99 USD

PURCHASE SOLUTION

Already member?


Option 2

Custom new solution created by our subject matter experts

GET A QUOTE