7676 Open source projects that are alternatives of or similar to Zhihu

머신러닝 입문자 혹은 스터디를 준비하시는 분들에게 도움이 되고자 만든 repository입니다. (This repository is intented for helping whom are interested in machine learning study)

A curated list of dedicated resources and applications

CNN image style transfer 🎨.

Semi-Supervised Video Object Segmentation (VOS) with Tensorflow. Includes implementation of *MaskRNN: Instance Level Video Object Segmentation (NIPS 2017)* as part of the NIPS Paper Implementation Challenge.

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

Deep learning codes and projects using Python

Multilingual word vectors in 78 languages

Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)

PyTorch tutorial for learners

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Neural Paiters

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

TensorFlow Tutorials

Official PyTorch implementation of "Artist Style Transfer Via Quadratic Potential"

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

Machine Reading Comprehension in Tensorflow

🙄 Difficult algorithm, Simple code.

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press

Implementations for a family of attention mechanisms, suitable for all kinds of natural language processing tasks and compatible with TensorFlow 2.0 and Keras.

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

TensorFlow 101: Introduction to Deep Learning for Python Within TensorFlow

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow

Important paper implementations for Question Answering using PyTorch

Deploy and scale serverless machine learning app - in 4 steps.

TensorFlow and Deep Learning Tutorials

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

How to build RNNs and LSTMs from scratch with NumPy.

MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)

Build a CNN network to predict 3D bounding box of car from 2D image.

Traffic Sign Detection. Code for the paper entitled "Evaluation of deep neural networks for traffic sign detection systems".

Virtual Clothing Try-on with Deep Learning. PyTorch reproduction of SwapNet by Raj et al. 2018. Now with Docker support!

Coding exercises for the Natural Language Processing concentration, part of Udacity's AIND program.

A PyTorch implementation of GraphRel

Graph Convolution Network for NLP

Oxford Deep NLP 2017 course - Practical 1: word2vec

Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

Official PyTorch Implementation for "Rotate to Attend: Convolutional Triplet Attention Module." [WACV 2021]

On the "steerability" of generative adversarial networks

Machine Learning notebooks for refreshing concepts.

Implementation of a seq2seq model for summarization of textual data. Demonstrated on amazon reviews, github issues and news articles.

Based on the Pytorch-Transformers library by HuggingFace. To be used as a starting point for employing Transformer models in text classification tasks. Contains code to easily train BERT, XLNet, RoBERTa, and XLM models for text classification.

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

KoBERT와 CRF로 만든 한국어 개체명인식기 (BERT+CRF based Named Entity Recognition model for Korean)

NeMo: a toolkit for conversational AI

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

Natural Language Toolkit for bahasa Malaysia, https://malaya.readthedocs.io/

Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)

Deep Learning Autonomous Car based on Raspberry Pi, SunFounder PiCar-V Kit, TensorFlow, and Google's EdgeTPU Co-Processor

Representation learning for link prediction within social networks

Toolkit to obtain and preprocess german corpora, train models using word2vec (gensim) and evaluate them with generated testsets

Simple Implementation of many GAN models with PyTorch.

Generative Adversarial Networks in TensorFlow 2.0

Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

Source code for "Transferring the Style of Homophonic Music Using Recurrent Neural Networks and Autoregressive Model"

PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

Library of state-of-the-art models (PyTorch) for NLP tasks