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Learn how to design, develop, deploy and iterate on production-grade ML applications.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
A game theoretic approach to explain the output of any machine learning model.
This repository contains the source code for the paper First Order Motion Model for Image Animation
🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
Build your neural network easy and fast, 莫烦Python中文教学
Image restoration with neural networks but without learning.
COCO API - Dataset @ http://cocodataset.org/
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
A scikit-learn compatible neural network library that wraps PyTorch
Efficient Image Captioning code in Torch, runs on GPU
Tacotron 2 - PyTorch implementation with faster-than-realtime inference
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
A simplified implemention of Faster R-CNN that replicate performance from origin paper
Language-Agnostic SEntence Representations
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
A small package to create visualizations of PyTorch execution graphs
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)
This is code of book "Learn Deep Learning with PyTorch"
Structured state space sequence models
Python libraries for Google Colaboratory
An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
pytorch1.0 updated. Support cpu test and demo. (Use detectron2, it's a masterpiece)
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.