Abdullah Mobeen's repositories
flytekit
Extensible Python SDK for developing Flyte tasks and workflows. Simple to get started and learn and highly extensible.
aybidi.github.io
portfolio
flyteplugins
Flyte Backend Plugins contributed by the Flyte community.
cli-telemetry
cli telemetry collection for platform products
flyte-experiments
Personal repo to experiment with Flyte
social-political-economic
Exploring Pakistan's issues through data
NLP-Landscape
Learning NLP
Moneyball
Using Moneyball on English Premier League
Algorithmic-Sprint
Just a bunch of algorithmic questions solved, some with explanation.
AlphaZero-Tutorial
Tutorial on AlphaZero
CTCI
Solutions to CTCI 6th Edition
Data-Structures
Implementation of Data Structures and Algorithms
Statistical_Inference
Coding up concepts from Larry Wasserman's All of Statistics.
Operating-Systems
Coding Assignments for Operating Systems
SIA
SIA is a data driven platform that recommends students on ideal career paths and graduate schools using information they wish to share.
jupyter-intro-xfel-jan17
Introduction to Jupyter Notebook
Mini-Camelot-AI
Implementation of Mini Camelot Board game using Mini-Max Algorithm with Alpha-Beta Pruning.
Eigenfaces
Eigenfaces and PCA (Ideas behind Face Recognition)
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.
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.
lstm_stock_prediction
This is an LSTM stock prediction using Tensorflow with Keras on top.