Deep Learning, NLP, NMT, AI, ML
To reproduce the results from paper, do the following steps. install pytorch and sparceconov. The website for sparse convolution is here Download the processed dataset from here and unzip it. You would have a folder structure like the following: . +– data +– src +– readme.md +– data.zip cd src ./sh/ad/train00.sh Try different script in the folder sh to reproduce the results in the paper. GitHub View Github
Read more
ℹ️ Reasoning If you use vim or neovim on a daily basis and work in large codebases, it is probably not uncommon for youto have 10+ tabs open at a time, with various splits. Once you close this vim session the layout is lost to the ethers.the mksession command in vim(neovim) can save you, thus saving the session to a directory, promising to return you to yourwork exactly how you left it. However, the problem is most of us accrue […]
Read more
-Undergraduate Thesis-Updated January 25, 2022 Author: Matteo Tanzi Year: 2022 Purpose: Undergraduate Thesis Currently, algorithms for point-based, line-based and dynamic registrations are being developped to be ported into 3D Slicer as modules Github Repository Structure This github repository contains Jupyter Notebooks and Python Programs The Python Programs are found within the programs folder – descriptions can be found below The Jupyter Notebooks – Found within Documentation – will outline the research and development techniques used for the point-set registration programs, […]
Read moreThis repo hosts my code for the article “Analyze Big Sequence Alignments with PySpark in AWS EMR”. Spark AWS CLI AWS Account Follow the instruction in the article. Once you have uploaded the files into your S3 bucket, run aws emr create-cluster –name “Spark_step_pip” –release-label emr-6.5.0 –applications Name=Spark –log-uri s3://[your_S3_bucket]/logs/ –instance-type m5.xlarge –instance-count 3 –bootstrap-actions Path=s3://[your_S3_bucket]/emr_bootstrap.sh –use-default-roles –auto-terminate –steps “Type=Spark,Name=SparkProgram,ActionOnFailure=CONTINUE,Args=[–deploy-mode,cluster,–master,yarn,–py-files,s3://[your_S3_bucket]/helper_function.py,s3://[your_S3_bucket]/spark_3mer.py,s3://[your_S3_bucket]/test.sam,[your_S3_bucket],sankey.json]”
Read moreHerzlich Willkommen zu dieser Hausaufgabe für unseren MOOC: Let’s Git! Wir hoffen, dass Du viel lernen wirst und dabei auch Spaß hast. In dieser Hausaufgabe wirst du eine Webseite zu deinem Lieblingscharakter aus Filmen oder Büchern erstellen. Hier kannst du zwei Beispiele betrachten: Beispiel 1 und Beispiel 2. Du wirst hier den Github Flow üben, aber das wirst du dann in den Aufgaben erkennen. Um die Aufgabe zu starten, folge einfach folgenden Schritten: Nutze dieses Repository als Vorlage für ein […]
Read moreA Celery application to collect data, download media and extract information from social media APIs. Requirements You must have a Redis DB instance running (which is currently used as the Celery broker). Also, another DB will probably be used as the Celery backend in the future(if we ever need backend, that is). You must also have a MongoDB instance running as the application’s write layer. Usage Run celery: celery -A app.core.celery.worker worker -l info Note, that Celery does not support […]
Read more$ apt update$ apt upgrade$ apt install python$ apt install git$ apt install dnsutils$ git clone https://github.com … Hammer need the Name Server of a website which you want to attack…To get the Name Server…just type$ nslookup example.comNote the IP Address of that Website then $ cd Hammer$ python hammer.py -s [ip Address] -t 135example:$ python hammer.py -s 123.45.67.89 -t 135 GitHub View Github
Read moreIn Python, a for loop is usually written as a loop over an iterable object. This means that you don’t need a counting variable to access items in the iterable. Sometimes, though, you do want to have a variable that changes on each loop iteration. Rather than creating and incrementing a variable yourself, you can use Python’s enumerate() to get a counter and the value from the iterable at the same time! In this course, you’ll see how to: Use […]
Read more
This is the implementation of our paper “Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks“. Dataset To successfully test performance, we created TPIC Dataset, a temporal popularity image collection dataset. Overview Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently […]
Read more