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100000000-instructions-evaluations-in-linux

This project made used of a 2 level cache - L1: split cache and L2: unified cache. I evaluated the impact of changing the cache configuration, such as L1 and L2 Cache size, Associativity, Block Size and Latency hit time. This was done using a SimpleScalar “sim-outorder” model and the SPEC 2000 benchmark suite. The result was compared using: Number of instructions per cycle (IPC), L1 data cache miss rate L1 instruction cache miss rate L2 unified cache miss rate The number of load instructions executed Total Simulation time

cnn-based-facial-expression-detection

This model uses Convolutional Neural Network to learn 8 different facial expression such as "neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness" and "surprise".

dynamically-allocating-memory

In here I created a simple matrix transpose solver. The special part of this is that the user can input the size(any size) of the matrix they want to transpose and also the inputs of each elements of the matrix. I had to dynamically allocate the rows and columns to this matrix since a matrix is a 2D array whose size must be allocated when created with a constant number, rather than a variable, in which the "cin" function takes.

histogram-generator

Simple image histogram generator using MATLAB

inverting-string-c-

This is just a simple c++ code for inverting string

kaggle-titanic-prediction

This is a project which predict the possibility of a passenger in the Titanic ship surviving or not. Information can be found on the website listed

mammogram-mass-project

This is a project use to describe if a mammogram is bening or malignant. The data set is from the uci repository and this is my final project implementation for the sundog frank kane udemy data science course. The implementation was well visualized and explaine for both experts and beginners. It also contains link to various models or methods used.

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

mnist-data-set

This is an efficient implementation of the Mnist data set which provides an accuracy of up to 98%

nested-structure-with-pointers

This is a simple c++ project using pointers on nested structures

recipes

reflections

seg-by-thresh

image segmentation by thresholding

sparse-matrix

surveliance-system

In this project, the Robotic Arm Edge is used to perform surveillance on a red brick. The robot detects the red brick using image segmentation by thresholding, the robot is moved using two degress of freedom to keep the camera at the centroid of the brick as the brick is moved. At the point where the robot lose track of the brick, an alarm starts buzzing.

udemy-frank

Udemy lectures on data science & machine learning; Sundog Education by Frank Kane