q-learning-algorithms
d2l-en
| q-learning-algorithms | d2l-en | |
|---|---|---|
| 1 | 6 | |
| 4 | 21,704 | |
| - | 1.3% | |
| 0.0 | 8.5 | |
| almost 3 years ago | 11 days ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
q-learning-algorithms
-
actor-critic algorithms
I learn quite some things about reinforcement learning in the last months, and I feel like I understand much better deep-Q learning algorithms (if you want, you can check my [repo](https://github.com/thomashirtz/q-learning-algorithms). I would like to change a little bit my focus towards actor-critics algorithms now. The only thing is, I feel like in comparison to Q-learning algorithms, the explanations of the papers are not as precise as for Q-learning, and explanations on the internet diverge really greatly (e.g. the original paper does not give the A2C but only the A3C for one learner).
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.
-
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
-
The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
-
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?
bomberland - Bomberland: a multi-agent AI competition based on Bomberman. This repository contains both starter / hello world kits + the engine source code
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
chess - Program for playing chess in the console against AI or human opponents
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
fragile - Framework for building algorithms based on FractalAI
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
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)
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)