Tool

A Tool For Direction Brute Force And Gathering Direction by Dork

XDGe XDGe It’s Tool For Direction Brute Force And Gathering Direction by Dork IF You Want GoogleDork Function? def GOscan(URLTARGET,DORKUSERFILE): DOKERLIST = [] GODOKERFILE = open(DORKUSERFILE,”r”) for CONGODOKER in GODOKERFILE: DOKERLIST.append(CONGODOKER) for CONDOKERLIST in range(len(DOKERLIST)): DORKTARGET = DOKERLIST[CONDOKERLIST].replace(” “,”+”).replace(“n”,””) SETGODORK = “site:{URLTARGET}+{DORKTARGET}”.format(URLTARGET=URLTARGET, DORKTARGET=DORKTARGET) SETGOWEBS = ‘https://www.google.com/search?q=’+SETGODORK+’&client=firefox-b-d&start=0’ REQGODORK = urllib.request.Request(SETGOWEBS) REQGODORK.add_header(‘User-Agent’, ‘Mozilla/7000.0 XAR’) REQGODORK.add_header(‘Accept’, ‘text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8’) REQGODORK.add_header(‘Accept-Language’, ‘en-US,en;q=0.8’) #——————————————— REQGOOGLE = urllib.request.urlopen(REQGODORK, timeout=10) RESGODORK = REQGOOGLE.read() REQGOOGLE.close() CLEARREQD = re.findall(‘

Read more
Machine Learning

A Katherine Crowson VQGAN+CLIP derived Google colab notebook

VQGAN-CLIP A repo for running VQGAN+CLIP locally. This started out as a Katherine Crowson VQGAN+CLIP derived Google colab notebook. Some example images: Environment: Tested on Ubuntu 20.04 GPU: Nvidia RTX 3090 Typical VRAM requirements: 24 GB for a 900×900 image 10 GB for a 512×512 image 8 GB for a 380×380 image Still a work in progress – I’ve not actually tested everything yet 🙂 Example set up using Anaconda to create a virtual Python environment with the prerequisites: conda […]

Read more
Machine Learning

A library for Multi-objective optimization in Gradient Boosted Trees

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives. With AL, we introduce dual variables in Boosting. The dual variables are iteratively optimized and fit within the Boosting iterations. The Boosting objective function is […]

Read more
Email

Heimdall Watchtower: Training Progress Email Notifier

Training-Progress-Email-Notifier Heimdall watchtower automatically sends you emails to notify you of the latest progress of your deep learning programs. In this way, you will know how the training goes wherever you are. You will also know as soon as your program has been terminated by other users 🙂 Usage Quick Start Simply run python email_test.py Please note: The test email is sent through a temporal gmail account. It may reach its capacity for sending emails through the SMTP server. In […]

Read more
Machine Learning

Generates vivid and realistic painting artworks with controllable styles in python

Stylized Neural Painting Official PyTorch implementation of the preprint paper “Stylized Neural Painting”, accepted to CVPR 2021.We propose an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering. Since a typical vector render is not […]

Read more
Advanced, NLP, Project, Python, Text, Unstructured Data

Part 2: Step by Step Guide to NLP – Knowledge Required to Learn NLP

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In part-1 of this blog series, we complete the basic concepts of NLP. Now, in continuation of that part, in this article, we will cover some of the new concepts. In this article, we will understand the knowledge required and levels of NLP in a detailed manner. In the last of this article, we […]

Read more
Beginner, Data Cleaning, Machine Learning, NLP, Python, Text, Word Embeddings

Part 5: Step by Step Guide to Master NLP – Word Embedding and Text Vectorization

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). Up to the previous part of this article series, we almost completed the necessary steps involved in text cleaning and normalization pre-processing. After that, we will convert the processed text to numeric feature vectors so that we can feed it to computers for Machine Learning applications. NOTE: Some concepts included in the pipeline of […]

Read more
Advanced, conversational AI, NLP

Memory Networks for Q&A(Question and Answer) Applications

This article was published as a part of the Data Science Blogathon Introduction This article shows the power of Memory Networks for Question and Answer (QA) applications in the context of simple natural language-based reasoning. Table of Contents What is the motivation behind Memory Networks? Why do we need Memory Networks when traditional NLP models are already performing well? Facebook bAbI dataset About Supporting Fact Components of Memory Networks How can we find the best match? How does the dot product […]

Read more
Advanced, Classification, Libraries, NLP, Project, Python, Structured Data, Supervised, Text

Sentiment Analysis using NLTK – A Practical Approach

This article was published as a part of the Data Science Blogathon Introduction The ultimate goal of this blog is to predict the sentiment of a given text using python where we use NLTK aka Natural Language Processing Toolkit, a package in python made especially for text-based analysis. So with a few lines of code, we can easily predict whether a sentence or a review(used in the blog) is a positive or a negative review. Before moving on to the implementation […]

Read more
Classification, News, NLP, Project, Python, Text

Build Your Own Fake News Classifier With NLP

img src: https://wallpapercave.com/w/wp7461543 Introduction The major objective of watching or reading news was to be informed about whatever is happening around us. There are several social media platforms in the current modern era, like Facebook, Twitter, Reddit, and so forth where millions of users would rely upon for knowing day-to-day happenings. Then came the fake news which spread across people as fast as the real news could. Fake news is a piece of incorporated or falsified information often aimed at misleading […]

Read more
1 … 561 562 563 564 565 … 912