🎥 Deep Q learning is Easy in PyTorch (Tutorial)
👁 1 раз ⏳ 2055 сек.
👁 1 раз ⏳ 2055 сек.
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4
#DeepQLearning #PyTorch #ReinforcementLearning
In this tutorial you will code up the simplest possible deep q network in PyTorch. We'll also correct some minor errors from previous videos, which were rather subtle.
You'll see just how easy it is to implement a deep Q network in Pytorch and beat the lunar lander environment. The agent goes from crashing on the lunar surface to landinVk
Deep Q learning is Easy in PyTorch (Tutorial)
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4
#DeepQLearning #PyTorch #ReinforcementLearning
In this tutorial you will code up the simplest possible deep q network in PyTorch.…
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4
#DeepQLearning #PyTorch #ReinforcementLearning
In this tutorial you will code up the simplest possible deep q network in PyTorch.…
Mini Course in Deep Learning with #PyTorch for AIMS
#course #DL
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
🔗 Atcold/pytorch-Deep-Learning-Minicourse
Minicourse in Deep Learning with PyTorch. Contribute to Atcold/pytorch-Deep-Learning-Minicourse development by creating an account on GitHub.
#course #DL
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
🔗 Atcold/pytorch-Deep-Learning-Minicourse
Minicourse in Deep Learning with PyTorch. Contribute to Atcold/pytorch-Deep-Learning-Minicourse development by creating an account on GitHub.
GitHub
GitHub - Atcold/NYU-DLSP20: NYU Deep Learning Spring 2020
NYU Deep Learning Spring 2020. Contribute to Atcold/NYU-DLSP20 development by creating an account on GitHub.
ResNeXt models pre-trained on Instagram hashtags stand out in their
in their ability to generalized to the 'ImageNetV2' test set
#PyTorch
https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/GeneralizationToImageNetV2.ipynb/
🔗 Google Colaboratory
in their ability to generalized to the 'ImageNetV2' test set
#PyTorch
https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/GeneralizationToImageNetV2.ipynb/
🔗 Google Colaboratory
Google
Google Colaboratory
Applied Deep Learning with #PyTorch - Full Course
https://www.youtube.com/watch?v=CNuI8OWsppg
🎥 Applied Deep Learning with PyTorch - Full Course
👁 1 раз ⏳ 20404 сек.
https://www.youtube.com/watch?v=CNuI8OWsppg
🎥 Applied Deep Learning with PyTorch - Full Course
👁 1 раз ⏳ 20404 сек.
In this course you will learn the key concepts behind deep learning and how to apply the concepts to a real-life project using PyTorch and Python.
You'll learn the following:
⌨️ RNNs and LSTMs
⌨️ Sequence Modeling
⌨️ PyTorch
⌨️ Building a Chatbot in PyTorch
⭐️Requirements ⭐️
⌨️ Some Basic High School Mathematics
⌨️ Some Basic Programming Knowledge
⌨️ Some basic Knowledge about Neural Networks
⭐️Contents ⭐️
⌨️ (0:00:08) Recurrent Nerual Networks - RNNs and LSTMs
⌨️ (0:35:54) Sequence-To-Sequence Models
⌨️YouTube
Applied Deep Learning with PyTorch - Full Course
In this course you will learn the key concepts behind deep learning and how to apply the concepts to a real-life project using PyTorch and Python.
You'll learn the following:
⌨️ RNNs and LSTMs
⌨️ Sequence Modeling
⌨️ PyTorch
⌨️ Building a Chatbot in PyTorch…
You'll learn the following:
⌨️ RNNs and LSTMs
⌨️ Sequence Modeling
⌨️ PyTorch
⌨️ Building a Chatbot in PyTorch…
BoTorch: Programmable Bayesian Optimization in PyTorch
Balandat et al.: https://arxiv.org/abs/1910.06403
Code: https://github.com/pytorch/botorch
#MachineLearning #Bayesian #PyTorch
🔗 BoTorch: Programmable Bayesian Optimization in PyTorch
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, molecular chemistry, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization. Enabled by Monte-Carlo (MC) acquisition functions and auto-differentiation, BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, radically simplifying implementation of novel acquisition functions. Our MC approach is made practical by a distinctive algorithmic foundation that leverages fast predictive distributions and hardware acceleration. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries. BoTorch is open source and available at https://github.com/pytorch/botorch.
Balandat et al.: https://arxiv.org/abs/1910.06403
Code: https://github.com/pytorch/botorch
#MachineLearning #Bayesian #PyTorch
🔗 BoTorch: Programmable Bayesian Optimization in PyTorch
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, molecular chemistry, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization. Enabled by Monte-Carlo (MC) acquisition functions and auto-differentiation, BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, radically simplifying implementation of novel acquisition functions. Our MC approach is made practical by a distinctive algorithmic foundation that leverages fast predictive distributions and hardware acceleration. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries. BoTorch is open source and available at https://github.com/pytorch/botorch.
arXiv.org
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We...
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
🔗 Welcome to Spinning Up in Deep RL! — Spinning Up documentation
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
🔗 Welcome to Spinning Up in Deep RL! — Spinning Up documentation
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
We just released our #NeurIPS2019 Multimodal Model-Agnostic Meta-Learning (MMAML) code for learning few-shot image classification, which extends MAML to multimodal task distributions (e.g. learning from multiple datasets). The code contains #PyTorch implementations of our model and two baselines (MAML and Multi-MAML) as well as the scripts to evaluate these models to five popular few-shot learning datasets: Omniglot, Mini-ImageNet, FC100 (CIFAR100), CUB-200-2011, and FGVC-Aircraft.
Code: https://github.com/shaohua0116/MMAML-Classification
Paper: https://arxiv.org/abs/1910.13616
#NeurIPS #MachineLearning #ML #code
🔗 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...
Code: https://github.com/shaohua0116/MMAML-Classification
Paper: https://arxiv.org/abs/1910.13616
#NeurIPS #MachineLearning #ML #code
🔗 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...
GitHub
GitHub - shaohua0116/MMAML-Classification: An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task…
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 - GitHub - sh...
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke et al.: https://arxiv.org/abs/1912.01703
#ArtificialIntelligence #deepLearning #PyTorch
🔗 PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.
Paszke et al.: https://arxiv.org/abs/1912.01703
#ArtificialIntelligence #deepLearning #PyTorch
🔗 PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.
Машинное обучение, AI, нейронные сети, Big Data (VK)
Tensors | Deep Learning with PyTorch
https://www.youtube.com/watch?v=hXMoTDoehFY
Tensors | Deep Learning with PyTorch
https://www.youtube.com/watch?v=hXMoTDoehFY
YouTube
Tensors | Deep Learning with PyTorch
Tensors | Deep Learning with PyTorchComplete playlist - Deep Learning with PyTorch: https://www.youtube.com/playlist?list=PL1w8k37X_6L8oJGLWdzeOSRVTI6mL8vw7#...
Data Science / Machine Learning / AI / Big Data (VK)
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Rozemberczki et al.: https://arxiv.org/abs/2104.07788
#MachineLearning #ArtificialIntelligence #PyTorch
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Rozemberczki et al.: https://arxiv.org/abs/2104.07788
#MachineLearning #ArtificialIntelligence #PyTorch
Forwarded from Machinelearning
PyTorch представил усовершенствованные методы Activation Checkpointing (AC), цель которых - снижение потребления памяти при обучении.
Традиционный подход в
eager mode сохраняет промежуточные активации для обратного прохода, что зачастую приводит к значительному расходу ресурсов. AC позволяет не сохранять эти тензоры, а вычислять их заново при необходимости, тем самым жертвуя вычислительным временем ради экономии памяти.Новая техника – Selective Activation Checkpoint (SAC). В отличие от обычного AC, который затрагивает всю выбранную область, SAC дает гранулярный контроль над тем, какие операции следует пересчитывать, а какие – сохранять. Это достигается за счет использования
policy_fn, определяющей, нужно ли сохранять результаты конкретной операции. SAC будет полезен для избегания перевычисления ресурсоемких операций, например, матричных умножений.Для
torch.compile стала доступна Memory Budget API. Эта функция автоматически применяет SAC с оптимальной политикой, исходя из заданного пользователем бюджета памяти (от 0 до 1). Бюджет 0 соответствует обычному AC, а 1 – поведению torch.compile по умолчанию. @ai_machinelearning_big_data
#AI #ML #Pytorch
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