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πŸ’‘This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave.

MATLAB 100.00%
coursera machine-learning deep-learning deep-neural-networks support-vector-machines linear-regression logistic-regression decision-trees anomaly-detection principal-component-analysis

coursera-ml's Introduction

Hello there πŸ‘‹

visitors Open Source Love

#!/usr/bin/python
# -*- coding: utf-8 -*-


class SoftwareEngineer:

    def __init__(self):
        self.name = "Zhenye Na"
        self.role = "Software Engineer"
        self.language_spoken = ["zh_CN", "en_US"]

    def say_hi(self):
        print("Thanks for dropping by, hope you find some of my work interesting.")


me = SoftwareEngineer()
me.say_hi()

πŸ“ Blogs

πŸ“” Latest Blog posts

πŸ”§ Technologies & Tools

Cloud Services:

AWS API Gateway DynamoDB AWS Lambda CloudWatch SQS

Programming Languages:

Java Python Go Rust

Tools and Services:

Kubernetes Docker

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πŸ—‚οΈ Highlight Projects

DA-RNN crnn-pytorch

coursera-ml's People

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coursera-ml's Issues

vectorize

If you vectorize cost and backprop, 50 epochs takes 2.4 seconds instead of 150 seconds.

Vectorized cost: replace this loop with

yy = zeros(size(y,1),num_labels);
yy(sub2ind(size(yy),1:length(y),y'))=1;
J = sum(sum(- yy .* log(a3) - (1 - yy) .* log(1 - a3)))/m;

Vectorized backprop: replace this loop with

d3 = a3 - yy;
d2 = (d3 * Theta2) .* [ones(m, 1) sigmoidGradient(z2)];
d2 = d2(:,2:end);
Theta1_grad = d2' * a1;
Theta2_grad = d3' * a2;

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