Ibtastic's Stars
tensorflow/models
Models and examples built with TensorFlow
sebastianruder/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
davidsandberg/facenet
Face recognition using Tensorflow
eriklindernoren/Keras-GAN
Keras implementations of Generative Adversarial Networks.
eriklindernoren/PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
xizhengszhang/Leetcode_company_frequency
Collection of leetcode company tag problems. Periodically updating.
oarriaga/face_classification
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
bramstein/fontfaceobserver
Webfont loading. Simple, small, and efficient.
oegedijk/explainerdashboard
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
EscVM/OIDv4_ToolKit
Download and visualize single or multiple classes from the huge Open Images v4 dataset
carlsborg/rcviz
Python call graph visualization for recursive functions.
SheikhRabiul/A-Deep-Learning-Based-Illegal-Insider-Trading-Detection-and-Prediction-Technique-in-Stock-Market
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf
rosinality/sagan-pytorch
Self-Attention Generative Adversarial Networks Implementation in PyTorch
georgeliu1998/ideal_profiles
New repo for the Ideal Data Science Profiles Project
jacobgil/jacobgil.github.io
Personal blog
arnab64/food-search-recipe-embeddings
Food item search on Elasticsearch using language models trained on gensim