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Tracking mentions began in Dec 2020.
How to pre-train BERT on different objective tasks using HuggingFace
2 projects | /r/deeplearning | 10 Apr 2022
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
I created a way to learn machine learning through Jupyter
2 projects | /r/learnmachinelearning | 30 Apr 2021
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
What are some alternatives?
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
ssd_keras - A Keras port of Single Shot MultiBox Detector
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
einops - Deep learning operations reinvented (for pytorch, tensorflow, jax and others)
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
ScanRefer - [ECCV 2020] ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
textgenrnn - Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.