XkhldY's Stars
TheAlgorithms/Python
All Algorithms implemented in Python
tensorflow/tensorflow
An Open Source Machine Learning Framework for Everyone
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
django/django
The Web framework for perfectionists with deadlines.
Homebrew/brew
🍺 The missing package manager for macOS (or Linux)
apache/predictionio
PredictionIO, a machine learning server for developers and ML engineers.
marcotcr/lime
Lime: Explaining the predictions of any machine learning classifier
h2oai/h2o-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
udacity/self-driving-car
The Udacity open source self-driving car project
BinRoot/TensorFlow-Book
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
JasperSnoek/spearmint
Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012
Azure-Samples/Azure-MachineLearning-DataScience
ttvand/Facebook-V
Cleaned code of the winning submission of the Kaggle Recruiting Competition
mkliegl/kaggle-Facebook-V
Second place solution for Facebook V competition on Kaggle
vrdmr/CS273a-Introduction-to-Machine-Learning
Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
ivansanchezvera/TrajectoryClustering
Several Trajectory and time series clustering Algorithms. Project features a hashing technique to approximate clustering in linear time, using Distance base hashing for DTW. Includes a variation of TRACLUS algorithm for my research project
davidrosenberg/ml2015
Website for DS-GA 1003: Machine Learning and Computational Statistics
jwf-zz/Humansense-Android-App
Humansense Data Collection Platform for Android
thekingofkings/gowalla-exp
The trajectory based analysis and experiment on gowalla dataset.
ovlaere/placing-text
Framework code for automated georeferencing of textual data
ericwayman/taxi_predictions
A project to predict taxi drop off locations from start location, user id and pick up time.
ifhuang/Gowalla
A simple visualization of Gowalla dataset.
velowander/human-activity-recognition
Cleaning Data project based on IWAAL 2012 study "Human Activity Recognition on Smartphones..."
XkhldY/taxi_predictions
A project to predict taxi drop off locations from start location, user id and pick up time.