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.

An NLP deep learning toolkit for building training pipelines. Tries to minimize the effort for constructing the training and inference stages. Defines modular building blocks of neural network components, and a suite of NLP models. The end goal is to make building a neural network as easy as playing with Legos. Supports English and Chinese.

A three-stage deep learning system which can figure out how to imitate a person's voice from as little as five seconds of recorded speech. Speaks with the deepfaked voice in realtime. The sample is used to condition an existing TTS model to sound like someone.

Running it inside a Docker container: https://sean.lane.sh/posts/2019/07/Running-the-Real-Time-Voice-Cloning-project-in-Docker/

A deep learning NLP modeling framework based on PyTorch. Text classifiers, sequence taggers, joint intent-slot models.

In this post, we’ll be looking at how we can use a deep learning model to train a chatbot on my past social media conversations in hope of getting the chatbot to respond to messages the way that I would.