First you'll convert the feature dataset into a numpy array.Then you should create a Sequential model with 2 layers. The first layer should be a Dense layer with 64 units, and the second layer should also be a Dense one with units set to 1.Once the model is set up, you should compile it with a MeanSquaredError() loss and the optimizer set to optimizers.Adam().Then, when you fit the dataset on abalone_features and abalone_features, make sure to set the following values: epochs=10, verbose=0.Finally, you should print both the history from fitting the model, and the model summary.

Statistics ChallengeIn the Python file, you will be creating a model to predict the age of abalone's from a subset of the abalone dataset which we proide in CSV format. Part of the program is already implemented, your goal is to set up the model, train the model on the training data set, and print out some results.These are the following details you need to implement:First you'll convert the feature dataset into a numpy array.Then you should create a Sequential model with 2 layers. The first layer should be a Dense layer with 64 units, and the second layer should also be a Dense one with units set to 1.Once the model is set up, you should compile it with a MeanSquaredError() loss and the optimizer set to optimizers.Adam().Then, when you fit the dataset on abalone_features and abalone_features, make sure to set the following values: epochs=10, verbose=0.Finally, you should print both the history from fitting the model, and the model summary.

While training a CNN model, you receive the OOM (out of memory) error message. What can you do to resolve the issue? Choose all that apply.4 pointsReduce the batch size Reduce the number of epochsReduce the number of filters in conv layersReduce the stride of conv layersReduce the number of layers

Consider an observed random sample of size n, w1, . . . , wn, from a normal distribution N(µ, σ2 ). To the 75 observations in the dataset Data-A1a.csv apply the EM algorithm to fit via maximum likelihood the two-component normal mixture density with common variances, To this end, Use an available program to fit this mixture model via the EM algorithm such as Mclust, which may be found on CRAN. Explicitly give the starting or starting points tried in your fitting of the EM algorithm and the stopping criterion adopted. how can use R studio set starting points tried in fitting of the EM algorithm and the stopping criterion

This question refers to the following code snippet, which assumes that all required libraries have been imported.Xtrain, Xtest, ytrain, ytest = train_test_split(X,y,test_size = 0.3)yhat = GaussianNB().fit(Xtrain,ytrain).predict(Xtest)acc = accuracy_score(ytest, yhat)This code uses with of available data used for training. It outputs the based on . Every time we run this code, we will get .

def recommend(features,feature_list): neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean') neighbors.fit(feature_list) distances, indices = neighbors.kneighbors([features]) return indices