Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
d2l-en
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control | d2l-en | |
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2 | 6 | |
114 | 21,759 | |
- | 1.6% | |
4.5 | 8.5 | |
12 months ago | 13 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
- MPC with Gaussian processes for data-efficient reinforcement learning
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[OC] Visualizations of the learning of probabilistic model predictive control for reinforcement learning
Link to repositery: https://github.com/SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
d2l-en
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
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
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
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?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Machine-Learning-Collection - A resource for learning about Machine learning & Deep Learning
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
ProSelfLC-AT - noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
Robo-Semantic-Segmentation - Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
ALAE - [CVPR2020] Adversarial Latent Autoencoders
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
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.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)