heydarimo/Contextual-Embedding-Using-RNNs heydarimo/Contextual-Embedding-Using-RNNs

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • Created over 2 years ago
  • Updated over 2 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
heydarimo/Contextual-Embedding-Using-RNNs
github

heydarimo

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encoding knowledge that transfers across languages. in this repository we tend to implement this concept using Recurrent Neural Networks.

More Repositories

1

Stock-Market-Prediction

in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predicting stock prices.
Jupyter Notebook
3
star
2

DQN-vs-DDQN-LunarLander-Environment

In this Repository, we intend to implement the DQN and also the DDQN algorithm in case of training an agent to solve the Lunar-Lander problem. there are lots of exciting results after training which have been attached.
Jupyter Notebook
2
star
3

Text-Generation-Using-Harry-Potter-Book

In this repository, we intend to implement a Recurrent Neural Network that used to generate text using our train data which is the “HARRY POTTER AND THE GOBLET OF FIRE “
Jupyter Notebook
2
star
4

Intelligent-Systems-Course

This repository contains materials and course projects during attending the Intelligent Systems Course, for more detailed information please have a look at my Final_Report files which have been separately uploaded for each of the projects and consist of all required information about the implementations, analyses, and anything else you may concern about that!
Jupyter Notebook
2
star
5

Deep-Learning-and-Neural-Networks-Course

This repository contains materials and course projects during attending the Intelligent Systems Course, for more detailed information please have a look at my Final_Report files which have been separately uploaded for each of the projects and consist of all required information about the implementations, analyses, and anything else you may concern about that!
Jupyter Notebook
2
star
6

Mohammad-Heydariii

Config files for my GitHub profile.
1
star
7

YOLOv5

In this project, I used YOLOv5 for a simple object detection task using python.
Jupyter Notebook
1
star
8

Implementing-VGG-19-Using-ImageNet

in this repository we get familiar with the Transfer Learning idea on the ImageNet dataset, in addition, we see how we can employ this vision to implement VGG-19 which is one of the common models of CNNs.
Jupyter Notebook
1
star
9

Segmentation-Using-DeepLab

Deep learning based semantic segmentation Using the Deeplab.
Jupyter Notebook
1
star
10

DCGAN

In this project, we tend to generate some high-quality paintings using the ABSTRACT-ART-GALLERY dataset according to the DCGAN concept!
Jupyter Notebook
1
star
11

VQ-VAE

In this project, we have implemented the VQ-VAE algorithm on both MNIST and CIFAR10 datasets considering MSELOSS and also NLLLOSE.
Jupyter Notebook
1
star
12

Segmentation-Using-FCN

Deep learning based semantic segmentation Using the FCN.
Jupyter Notebook
1
star
13

Convex-Optimization-Course

This repository contains materials and course projects during attending the Convex Optimization Course.
Jupyter Notebook
1
star
14

SGAN

The semi-supervised GAN, or SGAN, model is an extension of the GAN architecture that involves the simultaneous training of a supervised discriminator, unsupervised discriminator, and a generator model. The result is both a supervised classification model that generalizes well to unseen examples and a generator model that outputs plausible examples of images from the domain. in this repository we tend to implement a simplified formation of that.
Jupyter Notebook
1
star