aybidi Goto Github PK

algorithmic-sprint

Just a bunch of algorithmic questions solved, some with explanation.

alphazero-tutorial

Tutorial on AlphaZero

aybidi.github.io

portfolio

classifying-hand-written-digits---neural-networks

Another attempt at classifying handwritten digits (5000 this time), but this time using Neural Networks. It gave me percentage error a lot less than what I got using Support Vector Machines. To make this assignment easy, data is provided for hidden layer. This Neural Network contains one input layer with 400 nodes (pixels 20 x 20 of one image), one hidden layer with 25 nodes , and an output layer with 10 nodes (one for each number 0-9). There are 4 files containing data: ps5_data.csv ~ 5000 x 400 matrix of image data, ps5_data-labels.csv ~ 5000 x 1 vector of image labels (10 = "0" label), ps5_theta1.csv ~ 25 x 401 matrix for weights from input layer to hidden layer, and ps5_theta2.csv ~ 10 x 26 matrix for weights from hidden layer to output layer  Training data a data set with 5000 handwritten digits and their corresponding labels. Each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a number indicating the grayscale intensity at that location. Thus, your neural network will have 400 inputs.

classifying-hand-written-digits---support-vector-machines

A Machine Learning Project that aims to classify handwritten digits from 0 to 9. Support Vector Machine Algorithm is used to solve this challenge. The training set (mnist_train.txt) contains 2000 digits, and the test set (mnist_test.txt) contains 1000 digits. Each line represents an image of size 28×28 by a vector of length 784, with each feature specifying a grayscale pixel value. The first column contains the labels of the digits, 0–9, the next 28 columns represent the first row of the image, and so on. Gaussian Kernel is applied on Multiclass non-linear SVM to classify numbers in 10 classes (0-9). Different values of C and gamma parameters are used and then cross-validated to get the lowest error-percentage. The code takes some time to run because of the cross validation (5 folds) on 8 different values of C and gamma.

cli-telemetry

cli telemetry collection for platform products

covizvax

ctci

Solutions to CTCI 6th Edition

data-structures

Implementation of Data Structures and Algorithms

deep-learning---nyu

eigenfaces

Eigenfaces and PCA (Ideas behind Face Recognition)

flyte-experiments

Personal repo to experiment with Flyte

flytekit

Extensible Python SDK for developing Flyte tasks and workflows. Simple to get started and learn and highly extensible.

flyteplugins

Flyte Backend Plugins contributed by the Flyte community.

hard-and-soft-clustering

ilmademia_app

jupyter-intro-xfel-jan17

Introduction to Jupyter Notebook

lstm_stock_prediction

This is an LSTM stock prediction using Tensorflow with Keras on top.

mini-camelot-ai

Implementation of Mini Camelot Board game using Mini-Max Algorithm with Alpha-Beta Pruning.

moneyball

Using Moneyball on English Premier League

nlp-landscape

Learning NLP

operating-systems

Coding Assignments for Operating Systems

predicting-house-price---linear-regression

A Machine Learning assignment that predicts the house price using the data from 47 houses in Portland, Oregon. It uses the Linear Regression Algorithm. Data of the houses is contained in housing.txt.

predicting-the-market-value-of-football-players---anova-model

sia

SIA is a data driven platform that recommends students on ideal career paths and graduate schools using information they wish to share.

social-political-economic

Exploring Pakistan's issues through data

spotify-analytics

statistical_inference

Coding up concepts from Larry Wasserman's All of Statistics.

time-series-data-analysis-bitcoin