mdrahali's starred repositories
sim2realAI
We are indexing the progress in simulations to real world transfer for perception and control
vision-based-robotic-grasping
Related papers and codes for vision-based robotic grasping
tensor-house
A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
buyer_persona
Digital marketing tool helps to find out the buyer's persona by plotting its interest and suggesting marketing strategy to use
Friends-and-Enemies-of-Clinton
Stance detection, the task of identifying the speaker's opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task 6 on Detecting stance in tweets, which are related to the political campaign for the 2016 U.S. presidential elections: Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of the art, we evaluated our model against the dataset released in the SemEval-2016 Task 6 shared task competition. Our results outperform the best ones obtained by participating teams, and show that information about enemies and friends of politicians help in detecting stance towards them.
AI_project2_Marketing
Has graphical representation of "friends" and intelligently finding people/nodes to target for advertising based off how many connections/friends they have
EmotionBasedAdvertising
This is our project for Hack Cambridge Ternary. It is targeting adverts based on current human's emotion.
Finding-Influencers-in-Social-Networks
Finding Influential nodes in Social Network Graphs for targeted advertising
Market-Segmentation-using-Attributed-Graph-Community-Detection
Overview: Market segmentation divides a broad target market into subsets of consumers or businesses that have or are perceived to have common needs, interests, and priorities. These segments help firms or businesses focus on their target groups effectively and allocate resources efficiently. Traditional segmentation methods are solely based on attribute data such as demographics (age, sex, ethnicity, education, etc.) and psychographic profiles (lifestyle, personality, motives, etc.). However, social networks have recently become important for marketing. Depending on the nature of the market, social relations can even become vital in forming segments. Such social relations combined with demographic properties can be used to find more relevant subsets of consumers or businesses (i.e., communities).
High-Frequency-Trading-Model-with-IB
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
practical-cryptography-in-python
Source code for 'Cryptography in Python Source Code' by Seth Nielson and Christopher K. Monson
Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
backpropamine
Train self-modifying neural networks with neuromodulated plasticity
differentiable-plasticity
Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.
Real-Time-Person-Removal
Removing people from complex backgrounds in real time using TensorFlow.js in the web browser
Mastering-IOT
Build modern IoT solutions that secure and monitor your IoT infrastructure
hexo-theme-icarus
A simple, delicate, and modern theme for the static site generator Hexo.
build-your-own-x
Master programming by recreating your favorite technologies from scratch.
HackingNeuralNetworks
A small course on exploiting and defending neural networks