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-4-d-g-t-7-segment-d-splay

arabayarisi

awesome-chatbot

Awesome Chatbot Projects,Corpus,Papers,Tutorials.Chinese Chatbot =>:

beyazperdesentimentanalysis

teaching the positive, neutral and negative states of comments from theBeyazperde sites to the machine

chat-application

corona-outbreak-data-visualization

The software receives daily data on the coronavirus pandemic (COVID-19) provided by the European Center for Disease Prevention and Control (ECDC). EU Open Data Portal COVID-19 Coronavirus data set updated daily (https://opendata.ecdc.europa.eu/covid19/casedistribution/xml/) uses.

coronavirus-data-analysis

Covid-19-Data-Analysis according to regions

credit-ratings

cv.vbt.com.tr

data-structures-and-algorithm-project

emoji-search

Simple React app for searching emoji

fcm_push_notification

flutter

Flutter makes it easy and fast to build beautiful apps for mobile and beyond.

flutter-entry-examples

flutter_learn

Flutter Learn Series zero to end

flying-plane-for-android-studio

image-classification

image-search-design-project

java-project-visual-programming

kodluyoruz-frontend-101-egitimi

mp3-player

neural-machine-translation--nmt-

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. NMT departs from phrase-based statistical approaches that use separately engineered subcomponents.Neural machine translation (NMT) is not a drastic step beyond what has been traditionally done in statistical machine translation (SMT). Its main departure is the use of vector representations ("embeddings", "continuous space representations") for words and internal states. The structure of the models is simpler than phrase-based models. There is no separate language model, translation model, and reordering model, but just a single sequence model that predicts one word at a time. However, this sequence prediction is conditioned on the entire source sentence and the entire already produced target sequence. NMT models use deep learning and representation learning. The word sequence modeling was at first typically done using a recurrent neural network (RNN). A bidirectional recurrent neural network, known as an encoder, is used by the neural network to encode a source sentence for a second RNN, known as a decoder, that is used to predict words in the target language.Recurrent neural networks face difficulties in encoding long inputs into a single vector. This can be compensated by an attention mechanism which allows the decoder to focus on different parts of the input while generating each word of the output. There are further Coverage Models addressing the issues in such attention mechanisms, such as ignoring of past alignment information leading to over-translation and under-translation. Convolutional Neural Networks (Convnets) are in principle somewhat better for long continuous sequences, but were initially not used due to several weaknesses. These were successfully compensated for in 2017 by using "attention mechanisms". A attention-based model, the transformer architecture remains the dominant architecture for several language pairs.

nltk

notsistemi

parsing-xml-using-java-dom-parser

This file demonstrates how to parse an XML file using the Java programming language. We are using a DOM (Document Object Model) parser to find XML elements and print their values to the screen

poetrist_machine_lstm

Python code to create a text renderer using LSTMs.

project-using-r

python-classic-operations

some classic python operations

react

A declarative, efficient, and flexible JavaScript library for building user interfaces.

recognition-voices-withmachinelearning