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abedfiles

abednego97

Config files for my GitHub profile.

amazon-product-recommender-system

Sentiment analysis on Amazon Review Dataset available at http://snap.stanford.edu/data/web-Amazon.html

anyu

applied-data-science-capstone

applied-data-science-capstone-project

awesome-sentiment-analysis

😀😄😂😭 A curated list of Sentiment Analysis methods, implementations and misc. 😥😟😱😤

beginners-pytorch-deep-learning

Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

behavior-sequence-transformer-pytorch

This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

blog

blogs-and-tutorials

My web project

cartesiangeneticprogramming.jl

Cartesian Genetic Programming for Julia

chinese_cws_ner

中文分词和命名实体识别

daisyrec

A developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks

deep-ee-opt

Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, accepted for publication in IEEE Transactions on Signal Processing.

deep_learning_recommender_system

You can train a neural network with user ratings or purchases, and use it to make recommendations; deep learning can be very good at recognizing patterns in a way similar to how our brain may do it. It's good at things like image recognition and predicting sequences of events.Neural networks are fundamentally matrix operations and there are already well-established matrix factorization techniques for recommender systems that fundamentally do something similar. In SVD for example, we find matrices that we multiply together using weights that are learned from stochastic gradient descent, it's almost the same thing, just thought of in a different way. So yeah, you could think of recommender systems as looking for patterns, just very complex ones based on the behavior of other people. So a matrix factorization can be modeled as a neural network. I think the main reason to experiment with applying neural networks to recommender systems is that it lets us take advantage of all the rapid advances in the fields of AI and deep learning. Amazon, for example, has open-sourced a system called DSSTNE, that's D-S-S-T-N-E, which allows you to run huge neural networks that deal with sparse data, on a cluster, efficiently. They claim to be using this internally for their own recommender systems. There are also ways to use TensorFlow in a cluster, and take advantage of a whole fleet of GPUs. And there's always research on new topologies for neural networks that can lead to fresh insights on how to make better recommendations using them. In some cases, approaches using neural networks have been shown to outperform SVD already, even if it's by a rather small margin. So, let's dive into some ways you can apply neural networks to the problem of making recommendations.

deep_reinforcement_learning_course

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

deforestation

A machine learning exercise, using KNN to classify deforested areas

demo

WebProjects

ekfslam

This repo contains both a MATLAB and Python based implementation of the Extended Kalman Filter Simultaneous Localization And Mapping (EKFSLAM) algorithm.

evcf

Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms

github-slideshow

A robot powered training repository :robot:

gym-d2d

Device-to-Device (D2D) communication OpenAI Gym environment

handwritten-digits-classification-using-knn-multiclass_perceptron-svm

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

ibm_applied_datascience_capstone

IBM Applied Data Science Capstone project

lstm-sentiment-analysis

Sentiment Analysis with LSTMs in Tensorflow

lydroo

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

markdown-here

Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.

mec_offloading

Code for Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market paper https://www.mdpi.com/1999-5903/11/5/118/pdf

metarl-offloading