ydata-synthetic
pytorch-forecasting
| ydata-synthetic | pytorch-forecasting | |
|---|---|---|
| 60 | 9 | |
| 1,292 | 3,611 | |
| 2.8% | - | |
| 7.3 | 8.6 | |
| 6 days ago | 9 days ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
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.
ydata-synthetic
-
Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code
Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
- ydata-synthetic: NEW Data - star count:1083.0
-
I absolutely hate my internship
1: Try to work with what you have and augment your dataset (honestly, 10 points is crap)
-
Assessing the Quality of Synthetic Data with Data-Centric AI
Data Quality is key for all applications and models, and LLMs are no exception :) I've been working on a small community project with synthetic data (https://github.com/ydataai/ydata-synthetic) using ydata-synthetic, and it really shows! Underrepresentation (category imbalance) and missing data are two of the main issues!
-
SOMEBODY HELP ME!
The Data-Centric AI Community creates community projects from time to time and is probably willing to help you in your project.
-
Help for Data Scientist position
Join nice data communities and start networking.
-
How to become a beast in DS ?
You know what they say: "Tell me who your friends are, and I'll tell you who you are!". Hang out with DS beasts and learn from them :)
-
Hey guys, I have a few questions
Interesting question! I think our AI/ML devs at the Data-Centric AI Community could have nice perspectives for your to decide :)
-
Embarking on a Journey of 99 Data Science Projects - From Beginner to Expert
Sounds like an amazing journey! Feel free to add your projects on our awesome-python-for-data-science repo as you go! And in case you need a hand or feedback on the projects, we'll be happy to help at the Data-Centric AI Community.
-
Data science problems
The best to do is to get started with end-to-end projects in a collaborative environment (somewhat approaching real-world settings). You may find some interesting resources in this GitHub repository. The Data-Centric AI Community actually has a nice support system for this.
pytorch-forecasting
- FLaNK Stack Weekly for 14 Aug 2023
-
Pytorch Lstm
Source: Conversation with Bing, 4/5/2023 (1) jdb78/pytorch-forecasting: Time series forecasting with PyTorch - GitHub. https://github.com/jdb78/pytorch-forecasting. (2) Time Series Prediction with LSTM Using PyTorch - Colaboratory. https://colab.research.google.com/github/dlmacedo/starter-academic/blob/master/content/courses/deeplearning/notebooks/pytorch/Time_Series_Prediction_with_LSTM_Using_PyTorch.ipynb. (3) time-series-classification · GitHub Topics · GitHub. https://github.com/topics/time-series-classification. (4) PyTorch: Dataloader for time series task - Stack Overflow. https://stackoverflow.com/questions/57893415/pytorch-dataloader-for-time-series-task.
-
[D] What is the best approach to create embeddings for time series with additional historical events to use with Transformers model?
Temporal fusion transformer https://github.com/jdb78/pytorch-forecasting
-
LSTM/CNN architectures for time series forecasting[Discussion]
Pytorch-forecasting
- Can someone help me with this? It's been days that i struggle with this problem, Forecasting w DeepAR
- Can someone help me with this? it's been days that i struggle with this problem
-
[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].
To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast
-
When to go for an 'easy' time-series model vs. using a complex deep learning model (when having experience with the latter)
I'm a data trainee at this organisation. I wrote my master thesis about using an event clustering mechanism to enrich an existing dataset to improve short-term demand predictions, using Pytorch Forecasting using the temporal fusion transformer component, and LightGBM (and compare the models with and w/o the event feature, so 4 runs in total).
-
A python library for easy manipulation and forecasting of time series.
Darts is a pretty nice one. I've recently been using pytorch-forecasting for larger models like the Temporal Fusion Transformer. https://github.com/jdb78/pytorch-forecasting
What are some alternatives?
REaLTabFormer - A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Copulas - A library to model multivariate data using copulas.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
Conditional-Sig-Wasserstein-GANs
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
tslearn - The machine learning toolkit for time series analysis in Python