Discover and read the best of Twitter Threads about #pytorch

Most recents (24)

Following the @_useRconf 2022 conference and right before the RStudio conference, here are, in my opinion, the main trends in the #R language ๐ŸŒˆ:
โžก๏ธ MLOps ๐Ÿš€
โžก๏ธ Data ๐ŸŽ
โžก๏ธ Documentation โค๏ธ
๐Ÿงต๐Ÿ‘‡๐Ÿผ

#rstats #opensource #DataScience
MLOps - I was not aware of any #MLOps framework in R until the release of the ๐ฏ๐ž๐ญ๐ข๐ฏ๐ž๐ซ package. The vetiver package by @juliasilge from RStudio provides an MLOps framework for both R and #Python. Image
The vetiver supports MLOps applications for tidymodels, #XGBoost, mlr, caret in R, and for #PyTorch and scikit-learn in Python Image
Read 9 tweets
A few weeks ago I shared how you can animate a picture of yourself in #Python. Well!!! you can also animate a video of yourself too๐Ÿโœจ

#animeganv2forvideos is an open ML project that uses #PyTorch to let you do this. Here's what it looks like in my terminal. Full code below๐Ÿ‘‡๐Ÿฟ๐Ÿ’•
This code is based on code in the project's public collab notebookโœจ I edited it to run on a cpu and to be able to use it in my terminal. Pictures are in order๐Ÿ‘ฉ๐Ÿฟโ€๐Ÿ’ป

You can also have a look at the original code here github.com/nateraw/animegโ€ฆ ImageImageImage
As a final note, I found this awesome project through @huggingface's spaces๐Ÿค—๐Ÿ’•

If you don't want to write the code and just want to convert your video automatically or turn your webcam stream directly to animated video use the app here
huggingface.co/spaces/naterawโ€ฆ
Read 4 tweets
2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python
Read 16 tweets
2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python
Read 17 tweets
2/n
Following tips may boost model performance across different network structures with up to 5% (mAP or mean Average Precision) without increasing computational costs in any way.

#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #AI
3/n
Visually Coherent Image Mix-up for Object Detection. This has already been proven to be successful in lessening adversarial fears in network classification after testing it on COCO 2017 and PASCAL datasets with YOLOv3 models.
#computervision #pytorch
Read 13 tweets
NeuralForecast - new #Python library for time series forecasting with deep learning by @nixtlainc.
The package provides tools and applications for preprocessing time series data, statistical tests, modeling, #forecasting, and benchmark performance. ๐Ÿงต ๐Ÿ‘‡๐Ÿผ
#machinelearning
The package leveraged on the backend PyTorch and PyTorchLightning.

The package is fairly in early-stage, the first release, version 0.0.7, was two days ago. ๐ŸŒˆ

License: GPL-3
#PyTorch #opensource
The package provides the following forecasting models (1/2):
โœ… Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)
โœ… Exponential Smoothing Recurrent Neural Network (ES-RNN)
โœ… Neural Basis Expansion Analysis (N-BEATS)
Read 6 tweets
๐ƒ๐ž๐ž๐ฉ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐ข๐ง ๐๐ก๐š๐ซ๐ฆ๐š! ๐Ÿš€๐Ÿš€๐Ÿš€

๐€๐ฌ๐ญ๐ซ๐š ๐™๐ž๐ง๐ž๐œ๐š recently released ๐‚๐ก๐ž๐ฆ๐ข๐œ๐š๐ฅ๐— - an open-source ๐๐ฒ๐ญ๐ก๐จ๐ง library for deep learning applications for drug pair scoring. ๐Ÿงต ๐Ÿ‘‡๐Ÿผ

#python #PyTorch #pharma #DeepLearning #datascience
The package uses on the backend ๐๐ฒ๐“๐จ๐ซ๐œ๐ก and ๐“๐จ๐ซ๐œ๐ก๐ƒ๐ซ๐ฎ๐  (see links ๐Ÿ‘‡๐Ÿผ) for applications such as drug-drug interaction, polypharmacy side effects, and synergy prediction.

๐‹๐ข๐œ๐ž๐ง๐ฌ๐ž: Apache 2.0 ๐ŸŒˆ
๐‘๐ž๐ฌ๐จ๐ฎ๐ซ๐œ๐ž๐ฌ ๐Ÿ“š
Documentation: chemicalx.readthedocs.io/en/latest/
Source code: github.com/AstraZeneca/chโ€ฆ
Read 4 tweets
๐Ÿค–PyTorch is one of the most popular Deep Learning frameworks in use today. We have received numerous requests to cover topics related to Computer Vision in #PyTorch.

We hear you, here are our top free resources that will get you started. ๐Ÿš€
What is PyTorch?

๐Ÿ”—pyimagesearch.com/2021/07/05/whaโ€ฆ

โœจWhen to choose PyTorch
โœจUnderstand the differences with other DL libraries
โœจGPU support
โœจIs PyTorch more suitable for research?
Your First Neural Network with PyTorch

๐Ÿ”—pyimagesearch.com/2021/07/12/intโ€ฆ

โœจCreate NN architecture from scratch using PyTorch
โœจInitialize optimizers and loss function
โœจLooping over training epochs
โœจBackpropagate the network
Read 7 tweets
Are you looking to start with ๐Š๐ฎ๐›๐ž๐Ÿ๐ฅ๐จ๐ฐ ๐ŸŒˆ? check this step by step installation guide for Windows by Ashish Patel ๐Ÿงต๐Ÿ‘‡๐Ÿผ

github.com/ashishpatel26/โ€ฆ

#MLOps #ML #Kubernetes Image
Kubeflow is an open-source #MLOps tool that provides toolkits for the deployment of machine learning workflows on #Kubernetes ๐Ÿš€. Supports core data science tools such as Jupyter notebooks, training ML models such as #TensorFlow, #PyTorch, #XGBoost, setting pipelines, etc
The guide covers:
- #Docker installation
- Installation of Choco
- Installation and setting of Hyper-V
- Installation of #MiniKube and #Kubectl
Read 5 tweets
New release for @Uber forecasting library ๐ŸŒˆ

Orbit is an open-source #Python library for Bayesian time series forecasting and inference applications developed by @UberEng ๐Ÿงต ๐Ÿ‘‡๐Ÿผ

#timeseries #forecast #MachineLearning #PyTorch #Bayesian #Bayes
The library uses under the hood probabilistic programming languages with libraries such as #Python @mcmc_stan , Pyro, and #PyTorch to build the forecast estimators.

The new release, version 1.1 includes the following new features and changes: ๐Ÿ‘‡๐Ÿผ
New #forecasting model - Kernel Time-based #Regression (KTR). KTR model uses latent variables to define a smooth, time-varying representation of regression coefficients. Tutorials: ๐Ÿ‘‡๐Ÿผ
orbit-ml.readthedocs.io/en/latest/tutoโ€ฆ
orbit-ml.readthedocs.io/en/latest/tutoโ€ฆ
orbit-ml.readthedocs.io/en/latest/tutoโ€ฆ
orbit-ml.readthedocs.io/en/latest/tutoโ€ฆ
Read 8 tweets
Memory Efficient Coding in #PyTorch โšก
20 tricks to optimize your PyTorch code

Let's break some of the bad habits while writing PyTorch code ๐Ÿ‘‡

A thread ๐Ÿงต
1. PyTorch dataloader supports asynchronous data loading in a separate worker subprocess. Set pin_memory=True to instruct the DataLoader to use pinned memory which enables faster and asynchronous memory copy from host to GPU Image
2. Disable gradient calculation for validation or inference. Gradients aren't needed for inference or validation, so perform them within torch.no_grad() context manager. Image
Read 25 tweets
Pandas is a fast, powerful, flexible and open source data analysis and manipulation tool.

A Mega thread ๐Ÿงตcovering 10 amazing Pandas hacks and how to efficiently use it(with Code Implementation)๐Ÿ‘‡๐Ÿป
#Python #DataScientist #Programming #MachineLearning #100DaysofCode #DataScience
1/ Indexing data frames
Indexing means to selecting all/particular rows and columns of data from a DataFrame. In pandas it can be done using two constructs โ€”
.loc() : location based
It has methods like scalar label, list of labels, slice object etc
.iloc() : Interger based
2/ Slicing data frames
In order to slice by labels you can use loc() attribute of the DataFrame.

Implementation โ€”
Read 17 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (56.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (69.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (57.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python Image
I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python Image
I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.4%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (53.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
Hugging Face Transformers

Building transformer architecture remains an incredible challenge.

Hugging Face Transformers is one of the most popular and advanced frameworks for Transformer architectures.
โฌ‡๏ธ1/3 Image
Hugging Face Transformers provides

- implementations of 100s of pre-trained transformer models
- APIs to quickly download and use
- implementations for both #PyTorch and #TensorFlow
2/3
This is a thread from Edge#111 โ€“ our series about transformers.

Keep learning every day. Subscribe to our Twitter @TheSequenceAI.
3/3
Read 3 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (61.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (61.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (62.8%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (67.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (59.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (63.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Dailyโ€ฆ
#NLP #Python Image
I analyzed the sentiment on the last 517 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (62.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 517 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets

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