Unsupervised Learning - Kmeans Week 11 Challenge

The file has been attached below.

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I have applied LSTM and the file has been attached below. Thank You.

As mentioned in the lecture to find a dataset from opensource i have found my own dataset and i worked on that. The file has been attached below. Thank You.

Q.1 Explain vanishing gradient problem in RNN The vanishing gradient problem occurs when the backpropagation algorithm moves back through all of the neurons of the neural net to update their weights. The nature of recurrent neural networks means that the cost function computed at a deep layer of the neural net will be…

Q.1 Where do we use 2 level of padding?We use 2 level of padding when we want our output to have 1 level of padding (i.e an frame on an output image). Q.2 What is the formula to evaluate the dimension of output in terms of padding and stride?Formula = n + 2p - f + 1 / swhere n = input dimensionp = paddingf = filter layers…

Q.Identify the working principle of all 5 GBM and list down its advantages and disadvantages? AdaBoostAdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only…

I didn't find any dataset here and i want to try that what happens to regularization with single input feature so i used my own dataset. File has been attached below.

Q.Write steps for gradient descent for 2 input features and output having 3 classes. 1.Loss function = -[ yloga1 + y2loga2 + y3loga3 + y4loga4] 2.We have to found derivatives for 0th layer with respect to z 3.We have to found derivatives for 1st hidden layer by applying any activation function 4.As we know we have 3 classes…

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For Question No for 4 Python file has been attached For Question No 5 and 13 Excel file has been attached For all other questions SQL file has been attached

File has been attached for Project 1

Final Project : To perform gradient descent for more than one input features and on pen and paper also for 2 iteration. images and file has been attached.

As i couldn't find the data here so i have created my own data and performed the logistic regression using Simple and Stratified sampling. File has been attached for this question.

The file for Project 2 has been attached below Thank You.

The file for Project 1 has been attached below Thank You

1.What is a Neural Network?Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. 2.What is deep learning?Deep Learning is a subfield of machine learning…

1.How does similarity is calculated if data is categorical in natureThe similarity between categorical data is calculated as 0 and 1.if two data are same then it is 0 and if they are different it is 1. 2.Implement K-means for “Car dataset” and come up with the business insightsFile for this question has been…

1.Apply knn to the “Surface defects in stainless steel plates” and identify the differencesAccuracy for SVM with train test = 70% : 30% is 73%.Accuracy for KNN with train test = 70% : 30% is 72%.So here in KNN we are losing our accuracy by 1% compare to SVM.The file for 1 question has been attached below 2.What…

1.Pros and cons of SVMAdvantages:SVM works relatively well when there is a clear margin of separation between classes.SVM is more effective in high dimensional spaces.SVM is effective in cases where the number of dimensions is greater than the number of samples.SVM is relatively memory efficientDisadvantages:SVM algorithm…

1.Perform Gradient Descent in Python with any loss function m=0 b=0 l=0.001 epochs=1000 n = float(len(x)) for i in range(len(x)): y_pred = m*x + b d1 = (-2/n) * sum(x * (y-y_pred)) d2 = (-2/n) * sum(y-y_pred) m = m-l*d1 b = b-l*d2 print(m,b) 2.Difference between L1 & L2 Gradient descent method L1 = mean absolute error…

1.Calculate all 4 business moments using pen and paper for the below data set? For the 1 question images has been attached below 2.What is the significance of expected value when simple mean (Sum of all observations/number of observations) is already in place The mean is calculated for the uniform probability distribution…

1. Why there is a difference in the formula of variance for population and sample Formula of variance for population =∑(Xi−u)2/n Formula of variance for sample =∑(Xi−u)2/n-1 Because variance for population is calculated from the population data,and the variance for sample is calculated from sample data…

The file for project 2 has been attached below Thank You.

The file for project 1 has been attached below. Thank You