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IntroductionUsing support vector machines (SVMs) to build a spam classifi er.Files included in this exerciseex6.m - Octave/MATLAB script for the rst half of the exerciseex6data1.mat - Example Dataset 1ex6data2.mat - Example Dataset 2ex6data3.mat - Example Dataset 3svmTrain.m - SVM training functionsvmPredict.m - SVM prediction functionplotData.m - Plot 2D datavisualizeBoundaryLinear.m - Plot lin..

Introductionimplement regularized linear regression and use it to study models with different bias-variance properties.Files included in this exerciseex5.m - Octave/MATLAB script that steps you through the exerciseex5data1.mat - Datasetsubmit.m - Submission script that sends your solutions to our serversfeatureNormalize.m - Feature normalization functionfmincg.m - Function minimization routine (..

IntroductionImplement the back propagation algorithm for neural networks and apply it to the task of hand-written digit recognition.Files included in this exerciseex4.m - Octave/MATLAB script that steps you through the exerciseex4data1.mat - Training set of hand-written digitsex4weights.mat - Neural network parameters for exercise 4submit.m - Submission script that sends your solutions to our se..

IntroductionImplement one-vs-all logistic regression and neural networks to recognize hand-written digits.Files included in this exerciseex3.m - Octave/MATLAB script that steps you through part 1ex3 nn.m - Octave/MATLAB script that steps you through part 2ex3data1.mat - Training set of hand-written digitsex3weights.mat - Initial weights for the neural network exercisesubmit.m - Submission script..

Modeling and prediction for movies Setup Load packages library(ggplot2) library(dplyr) library(statsr) library(GGally) library(gmodels) library(SignifReg) library(magrittr) Load data load("movies.Rdata") Part 1: Data Generalizability : The data set is comprised of 651 randomly sampled movies produced and released before 2016. So it has generalizability. Causality : In the data set, Each variable..

Deep Neural Network for Image Classification: Application¶When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Hopefully, you will see an improvement in accur..

Building your Deep Neural Network: Step by Step¶Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want! In this notebook, you will implement all the functions required to build a deep neural network. In the next assignment, you will use the..

Planar data classification with one hidden layer¶Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. You will learn how to: Implement a 2-class classification neural network with a single hidden layer Use units with a non..

Logistic Regression with a Neural Network mindset v5 Logistic Regression with a Neural Network mindset¶Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. Instructions: Do not use loo..

Statistical inference with the GSS data Setup Load packages library(ggplot2) library(dplyr) library(statsr) library(magrittr) library(doBy) Load data load("gss.Rdata") Part 1: Data I think the data set is generalizable. Because the data set was sampled from GSS Data though it was pre-processed. In the ‘GSS.html’ documentation,"Unlike the full General Social Survey Cumulative File, we have remove..