In this article, I will take you through an explanation and implementation of all Machine Learning algorithms with Python programming language.
Machine learning algorithms are a set of instructions for a computer on how to interact with, manipulate, and transform data. There are so many types of machine learning algorithms. Selecting the right algorithm is both science and art.
Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience. Large-scale indexing and querying of unstructured data: video, image, long/short text, music, source code, etc. Decentralized architecture from day one. Scalable & cloud-native by design: enjoy containerizing, distributing, sharding, async, REST/gRPC/WebSocket.
PyCameraServer is a Flask video / image / Youtube / IP Camera frames online web-editor with live streaming preview for objects recognition, extraction, segmentation, resolution upscaling, styling, colorization, interpolation, using OpenCV with neural network models: YOLO, Mask R-CNN, Caffe, DAIN, EDSR, LapSRN, FSRCNN, ESRGAN.
Free and Open Source Machine Translation API. 100% self-hosted, no limits, no ties to proprietary services. Run your own API server in just a few minutes. Playing with it a little, it seems like it might be interesting to experiment with. Supports a couple of languages right now, but at least they're useful ones.
API docs: https://libretranslate.com/docs/
Language models are kept in a different repo: https://github.com/uav4geo/LibreTranslate-Models
Recovers passwords from pixelized screenshots.
This implementation works on pixelized images that were created with a linear box filter.
In this article I cover background information on pixelization and similar research.
Requires that the user supply a De Bruijn sequence of characters that could be expected to appear in the obfuscated text.
It won't be perfect but it'll probably get you within spitting distance.
EleutherAI is a grassroots AI research group aimed at democratizing and open sourcing AI research. Multiple projects and usable training corpora. F/OSS model called GPT-Neo.
Several spinoff projects to investigate.
Script that will detect if a stranger is trying to use your laptop or if a stranger/authorized driver is trying to drive your car. This script will detect the face, and send you an email if new user is not identified.
Fully automated decryption tool using natural language processing & artifical intelligence, along with some common sense. Input encrypted text, (hopefully) get the decrypted text back. You don't know, you just know it's possibly encrypted. Ciphey will figure it out for you. Ciphey can solve most things in 3 seconds or less.
Ciphey can even be imported as a module in your own Python code!
It's basically the cryptographer's workbench I was going to write while I was in Pittsburgh.
A generative adversarial network that takes images and re-does them in various anime studios' styles. GPU enabled. Download the pre-trained model to get a jumpstart.
A framework that instantiates AI/ML models more or less automagickally - one line of code. Can read training data from URLs as well as locally. Has a GUI also but it's an optional install. Sits on top of PyTorch.
The description's kind of weird, but it seems to use a neural network to generate pictures of someone based upon a picture that you give it. "Generate your lover with your photo," whatever that means.
An AI assisted online translation service, positioned as an alternative and rival to Google Translate. Translate text online, from a desktop application, or using their API (which has a separate billing plan - $5.56 per month, plus 1 cent per 500 characters). Supports 72 possible pairs of languages, with more being added. The basic account is about $10us per month (annual subscriptions are somewhat less per month, aggregate).
A face detector (not facial identification) deep learning system based upon OpenCV and Tensorflow. Optimized for CPU, not GPU operation but does have a tensorflow-gpu switch available. Can even identify faces that aren't edge-on or partially obscured.
Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
Stanford has open sourced a self-hosted, personal assistant system. Designed with privacy in mind. Speech recognition, analysis, task execution. They want to make so that it's easy and highly useful for everyone to integrate into their stuff. Can monitor things and filter for certain things. Aims for composability. Services (skills) are also open source and crowdsourced.
An application that uses AI and ML to intelligently strip the vocals out of music tracks. Written in Python, uses Tensorflow. The pre-trained model is included in the distribution. Use as a CLI tool or a library for your own code.
textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, textacy focuses primarily on the tasks that come before and follow after. Abstracts away the boilerplate for the stuff you actually care about.
A F/OSS natural language translation system that seems to want to give Google Translate a run for its money. The corpuses used for training appear to be crowdsourced, and I think you can download the trained models on their own. Aims to be self-hosted.
Installation docs: http://wiki.apertium.org/wiki/Installation
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