shantanuj Goto Github PK

2002moviebookingsystem

api_ner

API for Tensorflow model in Flask

basic_reinforcement_learning

An introduction series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

closerlookfewshot

source code to ICLR'19, 'A Closer Look at Few-shot Classification'

courses

fast.ai Courses

cove

create-your-own-adventure

deep-learning-1

Repo for the Deep Learning Nanodegree Foundations program.

dfl-cnn

This is a pytorch re-implementation of Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

drqa

Reading Wikipedia to Answer Open-Domain Questions

dsp

fake_net

Framework to detect and analyze fake news

fasttext

Library for fast text representation and classification.

fine_grained_sen_extraction

Domain adaptation techniques for fine grained sentiment extraction models (in TensorFlow)

finegrainedextraction

Experiments in fine grained opinion and aspect extraction. Domain adaptation techniques included.

fnc-1

football-crunching

Analysis and datasets about football (soccer)

generative-models

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

getoldtweets-python

Obtain and process tweets from given handle to only include conversation tweets

imagerec

imitating-primate-infant-visual-development-cnns

Official pytorch code for paper "What do CNNs gain by imitating the visual development of primate infants?"

instagram-profilecrawl

📝 quickly crawl the information (e.g. followers, tags etc...) of an instagram profile.

interactive-coding-challenges

Huge update! Interactive Python coding interview challenges (algorithms and data structures). Includes Anki flashcards.

kt_json2html

Java util to generate survey questions for kindtype mturk HIT

learningsimplealgorithms

We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using Q-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by Q-learning.

mdp

mem_absa

Aspect Based Sentiment Analysis using End-to-End Memory Networks

neuron_level_nnets

OOP neuron focused neural networks (bare bone numpy implementation)

ntu-mun-2015front

q_similarity