For something in between a pytorch and a karpathy/micrograd. This may not be the best deep learning framework, but it is a deep learning framework. Due to its extreme simplicity (<= 1000 lines of code), it aims to be the easiest framework to add new accelerators to, with support for both inference and training. Support basic ops and you get SOTA vision and language models.
Perspective is a free API that uses machine learning to identify toxic comments, making it easier to host better conversations online.
Acrossword is a small async wrapper around the SentenceBERT library. It has a convenient object-oriented API with two main purposes:
semantic search
- create miniature, powerful, cached semantic search engines from organised collections of documents
- easily serialise and deserialise those documents in a gzipped JSON format
- create documents from cleaned webpages and text files
- search using different levels of granularity – from a book, to a chapter, to a single sentence
zero-shot text classification
- simply provide examples of each class, or something as simple as "This sentence is about X", and it will quite reliably classify it correctly
It's useful if you want to avoid larger bloated libraries with capabilities you don't need, and comes with zero fuss.
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Data is transformed into vector representations for search (also known as embeddings).
A curated list of delightful Conversational AI resources.
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
Jina is a neural search framework that empowers anyone to build SOTA and scalable deep learning search applications in minutes. Native support for PyTorch/Keras/ONNX/Paddle. Build solutions in just minutes. Process, index, query, and understand videos, images, long/short text, audio, source code, PDFs, etc. Distributed architecture, scalable & cloud-native from day one. Same developer experience on both local and cloud.
Create UIs for your machine learning model in Python in 3 minutes. Quickly create customizable UI components around your models. Gradio makes it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images, pasting your own text, recording your own voice, etc. and seeing what the model outputs.
MiniTorch is a diy teaching library for machine learning engineers who wish to learn about the internal concepts underlying deep learning systems. It is a pure Python re-implementation of the Torch API designed to be simple, easy-to-read, tested, and incremental. The final library can run Torch code. The project was developed for the course Machine Learning Engineering at Cornell Tech.
Github: https://github.com/minitorch/
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.
Self hostable?
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.
CUDA enabled.
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/
Github: https://github.com/uav4geo/LibreTranslate
The back end appears to be written in Python. Examples of use are in Javascript, but that's not necessarily the way it has to be done.
Language models are kept in a different repo: https://github.com/uav4geo/LibreTranslate-Models
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.
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.
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.