#DeepLearning with #PyTorch
Download a free copy of the book and learn how to get started with #AI / #ML development using PyTorch
#Python
🔭 @DeepGravity
Download a free copy of the book and learn how to get started with #AI / #ML development using PyTorch
#Python
🔭 @DeepGravity
#NVIDIA Makes 3D #DeepLearning Research Easy with #Kaolin #PyTorch Library
At its core, Kaolin consists of an efficient suite of geometric functions that allow manipulation of 3D content. It can wrap into PyTorch tensors 3D datasets implemented as polygon meshes, point clouds, signed distance functions or voxel grids.
With their 3D dataset ready for deep learning, researchers can choose a neural network model from a curated collection that Kaolin supplies. The interface provides a rich repository of models, both baseline and state of the art, for classification, segmentation, 3D reconstruction, super-resolution and more.
Link to the article
🔭 @DeepGravity
At its core, Kaolin consists of an efficient suite of geometric functions that allow manipulation of 3D content. It can wrap into PyTorch tensors 3D datasets implemented as polygon meshes, point clouds, signed distance functions or voxel grids.
With their 3D dataset ready for deep learning, researchers can choose a neural network model from a curated collection that Kaolin supplies. The interface provides a rich repository of models, both baseline and state of the art, for classification, segmentation, 3D reconstruction, super-resolution and more.
Link to the article
🔭 @DeepGravity
An official #PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (#NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim
This project is an implementation of Multimodal Model-Agnostic #MetaLearning via Task-Aware Modulation, which is published in NeurIPS 2019. Please contact Shao-Hua Sun for any questions.
Model-agnostic meta-learners aim to acquire meta-prior parameters from a distribution of tasks and adapt to novel tasks with few gradient updates. Yet, seeking a common initialization shared across the entire task distribution substantially limits the diversity of the task distributions that they are able to learn from. We propose a multimodal MAML (MMAML) framework, which is able to modulate its meta-learned prior according to the identified mode, allowing more efficient fast adaptation. An illustration of the proposed framework is as follows.
Link
🔭 @DeepGravity
This project is an implementation of Multimodal Model-Agnostic #MetaLearning via Task-Aware Modulation, which is published in NeurIPS 2019. Please contact Shao-Hua Sun for any questions.
Model-agnostic meta-learners aim to acquire meta-prior parameters from a distribution of tasks and adapt to novel tasks with few gradient updates. Yet, seeking a common initialization shared across the entire task distribution substantially limits the diversity of the task distributions that they are able to learn from. We propose a multimodal MAML (MMAML) framework, which is able to modulate its meta-learned prior according to the identified mode, allowing more efficient fast adaptation. An illustration of the proposed framework is as follows.
Link
🔭 @DeepGravity
GitHub
shaohua0116/MMAML-Classification
An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim - shaohua0116...
Learning #ReinforcementLearning: #REINFORCE with #PyTorch!
Getting started with #PolicyGradients
Link
🔭 @DeepGravity
Getting started with #PolicyGradients
Link
🔭 @DeepGravity
Medium
Learning Reinforcement Learning: REINFORCE with PyTorch!
The REINFORCE algorithm is one of the first policy gradient algorithms in reinforcement learning and a great jumping off point to get into…
Image Data Augmentation for #TensorFlow 2, #Keras and #PyTorch with Albumentations in #Python
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
Curiousily
Image Data Augmentation for TensorFlow 2, Keras and PyTorch with Albumentations in Python - Adventures in Artificial Intelligence…
Learn how to augment image data for Image Classification, Object Detection, and Image Segmentation