💣New open-source recommender system from Facebook.
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109
#Facebook #DLRM #recommender #DL #PyTorch #Caffe
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109
#Facebook #DLRM #recommender #DL #PyTorch #Caffe
0.2 release of PyTorchPipe
Library for multi-modal deep learning pipelines, in a modular fashion for GPU and CPU.
Link: https://github.com/IBM/pytorchpipe
#IBM #PyTorch
Library for multi-modal deep learning pipelines, in a modular fashion for GPU and CPU.
Link: https://github.com/IBM/pytorchpipe
#IBM #PyTorch
GitHub
GitHub - IBM/pytorchpipe: PyTorchPipe (PTP) is a component-oriented framework for rapid prototyping and training of computational…
PyTorchPipe (PTP) is a component-oriented framework for rapid prototyping and training of computational pipelines combining vision and language - GitHub - IBM/pytorchpipe: PyTorchPipe (PTP) is a co...
Release of 27 pretrained models for NLP / NLU for PyTorch
Hugging Face open sources a new library that contains up to 27 pretrained models to conduct state-of-the-art NLP/NLU tasks.
Link: https://medium.com/dair-ai/pytorch-transformers-for-state-of-the-art-nlp-3348911ffa5b
#SOTA #NLP #NLU #PyTorch #opensource
Hugging Face open sources a new library that contains up to 27 pretrained models to conduct state-of-the-art NLP/NLU tasks.
Link: https://medium.com/dair-ai/pytorch-transformers-for-state-of-the-art-nlp-3348911ffa5b
#SOTA #NLP #NLU #PyTorch #opensource
PyTorch for research
PyTorch Lightning — The PyTorch Keras for ML researchers. More control. Less boilerplate.
Github: https://github.com/williamFalcon/pytorch-lightning
#PyTorch #Research #OpenSource
PyTorch Lightning — The PyTorch Keras for ML researchers. More control. Less boilerplate.
Github: https://github.com/williamFalcon/pytorch-lightning
#PyTorch #Research #OpenSource
GitHub
GitHub - Lightning-AI/pytorch-lightning: Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes. - Lightning-AI/pytorch-lightning
spaCy meets PyTorch-Transformers: Fine-tune BERT, XLNet and GPT-2
Including pretrained models.
Link: https://explosion.ai/blog/spacy-pytorch-transformers
Pip:
#Transformers #SpaCy #NLP #NLU #PyTorch #Bert #XLNet #GPT
Including pretrained models.
Link: https://explosion.ai/blog/spacy-pytorch-transformers
Pip:
pip install spacy-pytorch-transformers#Transformers #SpaCy #NLP #NLU #PyTorch #Bert #XLNet #GPT
explosion.ai
spaCy meets Transformers: Fine-tune BERT, XLNet and GPT-2 · Explosion
Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. You can now use these models in spaCy, via a new interface library we've developed that connects spaCy to Hugging Face's awesome implementations.
Great collections of Data Science learning materials
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
GANs from Scratch 1: A deep introduction.
Great introduction and tutorial. With code in PyTorch and TensorFlow
Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart
Great introduction and tutorial. With code in PyTorch and TensorFlow
Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart
Video on how Facebook continues to develop its #Portal device
How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.
Link: https://ai.facebook.com/blog/smart-camera-portal-advances/
#CV #DL #Segmentation
How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.
Link: https://ai.facebook.com/blog/smart-camera-portal-advances/
#CV #DL #Segmentation
Meta
How we’ve advanced Smart Camera for new Portal video-calling devices
We’ve used Detectron2, Mask R-CNN, and custom hardware integrations like foveated processing in order to make additional speed and precision improvements in the computer vision models that power Smart Camera.
PyTorch 1.3 released
- named tensors support
- general availability of Google Cloud TPU support
- captum - SOTA tools to understand how the importance of specific neurons and layers affect predictions made by the models
- crypten - a new research tool for secure machine learning with PyTorch
- many other improvements
Official announce: https://pytorch.org/blog/pytorch-1-dot-3-adds-mobile-privacy-quantization-and-named-tensors/
Captum website: https://www.captum.ai
CrypTen code: https://github.com/facebookresearch/CrypTen
#DL #PyTorch #TPU #GCP #Captum #CrypTen
- named tensors support
- general availability of Google Cloud TPU support
- captum - SOTA tools to understand how the importance of specific neurons and layers affect predictions made by the models
- crypten - a new research tool for secure machine learning with PyTorch
- many other improvements
Official announce: https://pytorch.org/blog/pytorch-1-dot-3-adds-mobile-privacy-quantization-and-named-tensors/
Captum website: https://www.captum.ai
CrypTen code: https://github.com/facebookresearch/CrypTen
#DL #PyTorch #TPU #GCP #Captum #CrypTen
pytorch.org
An open source machine learning framework that accelerates the path from research prototyping to production deployment.
🎓 Reinforcement Learning Course from OpenAI
Reinforcement Learning becoming significant part of the data scientist toolbox.
OpenAI created and published one of the best courses in #RL. Algorithms implementation written in #Tensorflow.
But if you are more comfortable with #PyTorch, we have found #PyTorch implementation of this algs
OpenAI Course: https://spinningup.openai.com/en/latest/
Tensorflow Code: https://github.com/openai/spinningup
PyTorch Code: https://github.com/kashif/firedup
#MOOC #edu #course #OpenAI
Reinforcement Learning becoming significant part of the data scientist toolbox.
OpenAI created and published one of the best courses in #RL. Algorithms implementation written in #Tensorflow.
But if you are more comfortable with #PyTorch, we have found #PyTorch implementation of this algs
OpenAI Course: https://spinningup.openai.com/en/latest/
Tensorflow Code: https://github.com/openai/spinningup
PyTorch Code: https://github.com/kashif/firedup
#MOOC #edu #course #OpenAI
GitHub
GitHub - openai/spinningup: An educational resource to help anyone learn deep reinforcement learning.
An educational resource to help anyone learn deep reinforcement learning. - openai/spinningup
Implementing Transfer Learning in PyTorch
Fine-tuning and feature extraction with PyTorch
Link: https://medium.com/analytics-vidhya/transfer-learning-in-pytorch-f7736598b1ed
#PyTorch #novice #entrylevel #beginner
Fine-tuning and feature extraction with PyTorch
Link: https://medium.com/analytics-vidhya/transfer-learning-in-pytorch-f7736598b1ed
#PyTorch #novice #entrylevel #beginner
Medium
Implementing Transfer Learning in PyTorch
Transfer Learning is a technique where a model trained for a certain task is used for another similar task.
Free book on #PyTorch
Book with all the information required to work with one of the most popular #dl frameworks.
Link: https://twitter.com/PyTorch/status/1197603717144432640?s=20
#book #manual
Book with all the information required to work with one of the most popular #dl frameworks.
Link: https://twitter.com/PyTorch/status/1197603717144432640?s=20
#book #manual
Twitter
PyTorch
To help developers get started with PyTorch, we’re making the 'Deep Learning with PyTorch' book, written by Luca Antiga and Eli Stevens, available for free to the community: https://t.co/KH52NW0Itl
Neighbourhood Components Analysis
a PyTorch implementation of Neighbourhood Components Analysis
NCA learns a linear transformation of the dataset such that the expected leave-one-out performance of kNN in the transformed space is maximized.
The authors propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
It can also learn low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, this classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them.
The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.
paper (only pdf): https://www.cs.toronto.edu/~hinton/absps/nca.pdf
github: https://github.com/kevinzakka/nca
#kNN #pca #nca #PyTorch
a PyTorch implementation of Neighbourhood Components Analysis
NCA learns a linear transformation of the dataset such that the expected leave-one-out performance of kNN in the transformed space is maximized.
The authors propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
It can also learn low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, this classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them.
The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.
paper (only pdf): https://www.cs.toronto.edu/~hinton/absps/nca.pdf
github: https://github.com/kevinzakka/nca
#kNN #pca #nca #PyTorch
