Deep Learning



Best matching news and articles for Deep Learning


Title Summary/Keywords
Univariate Time Series With Stacked LSTM, BiLSTM, and NeuralProphet
https://pub.towardsai.net/ | Yesterday
Content:
Deep LearningDeveloping Deep learning LSTM, BiLSTM models, and NeuralProphet for multi step timeseriesDeveloping LSTM, BiLSTM models, and NeuralProphet for time seriesPhoto by Nick Chong onUnsplashTable ofContentsIntroductionWha is TimeSeries What isLSTM What is Bidirectional LSTM What is NeuralProphet Lets Get Started With the StockDataModel Implementation PhaseModels Train amp validation LossConclusionReferenceIn you like to try something other than regression to solve your...


Keywords: time series, regression, lstm, algorithms
One more approach to optimize neural networks
https://towardsdatascience.com/ | Yesterday
Content:
Talking about Neural Architecture Search and own algorithm for optimizing neural network hyperparametersPreview image image byauthor In the last decade, neural network based solutions have become extremely popular. At the same time, deep learning is quite complex field, requiring high theoretical knowledge from experts. The industry needs quite lot of these specialists, but now there are not enough of them to satisfy the request. With this gap between supply and demand, special tools are em...


Keywords: hive, design, neural network, pytorch
A small framework mimics PyTorch using CuPy or NumPy
https://pythonawesome.com/ | Yesterday
Content:
CuPyTorchCuPyTorch x662F x4E00 x4E2A x5C0F x578B PyTorch xFF0C x540D x5B57 x6765 x6E90 x4E8E xFF1A x4E0D x540C x4E8E x5DF2 x6709 x7684 x51E0 x4E2A x4F7F x7528 NumPy x5B9E x73B0 PyTorch x7684 x5F00 x6E90 x9879 x76EE xFF0C x672C x9879 x76EE x901A x8FC7 CuPy x652F x6301 cuda x8BA1 x7B97 x53D1 x97F3 x4E0E Cool PyTorch x63A5 x8FD1 xFF0C x56E0 x4E3A x4F7F x7528 x4E0D x8D85 x8FC7 1000 x884C x7EAF Python...


Keywords: dl , lstm, python, pytorch
Pushing the Limits of Self-Supervised ResNets: DeepMinds ReLICv2 Beats Strong Supervised Baselines on ImageNet
https://syncedreview.com | Yesterday
Content:
A DeepMind research team proposes ReLICv2, which demonstrates for the first time that representations learned without labels can consistently outperform a strong, supervised baseline on ImageNet and even achieve comparable results to state-of-the-art self-supervised vision transformers (ViTs).The post Pushing the Limits of Self-Supervised ResNets: DeepMinds ReLICv2 Beats Strong Supervised Baselines on ImageNet first appeared on Synced....


Keywords: transformer, contrastive, self-supervised, resnet, machine
Explainable Defect Detection Using Convolutional Neural Networks: Case Study
https://pub.towardsai.net/ | Yesterday
Content:
Deep LearningTrain object detection model without having any bounding boxes labels. This post shows the power of Explainable AI.Image byAuthorDespite being extremely accurate, neural networks are not that widely used in the domains, where prediction explainability is requirement, such as medicine, banking, education, etc.In this tutorial, Ill show you how to overcome this explainability limitation for Convolutional Neural Networks. And it isby exploring, inspecting, processing, and visualizing...


Keywords: tpu, pre-trained, deep learning, tutorial
Artificial Neural Network in Laymans Terms
https://towardsdatascience.com?source=rss----7f60cf5620c9---4 | Today
Content:
nan...


Keywords: design, network, data science, classification
Predicting future values with RNN, LSTM, and GRU using PyTorch
medium.com | Yesterday
Content:
Photo by Nkululeko Jonas.In my previous blog, I helped you get started with building some of the Recurrent Neural Networks (RNN), such as vanilla RNN, LSTM, and GRU, using PyTorch. If you haven't seen it yet, I strongly suggest you look at it first, as I'll be building on some of the...


Keywords: network, lstm, neural network, pytorch
Transfer Learning using EfficientNet PyTorch
https://debuggercafe.com/ | Yesterday
Content:
In this post, we do transfer learning using EfficientNet PyTorch. Specifically, we use the EfficientNetB0 modelThe post Transfer Learning using EfficientNet PyTorch appeared first on DebuggerCafe....


Keywords: transfer learning, pytorch
A Neural Network for Solving and Generating University Level Mathematics Problems Using Program Synthesis
https://www.marktechpost.com/ | Yesterday
Content:
The AI research community widely believed that modern deep learning architectures were not 8220 intelligent 8221 enough to solve advanced mathematical problems. But, previous attempts to solve such tasks used transformers that were pretrained only on texts, resulting in their failures. Lacking the ability to automatically solve, grade, and generate university level mathematics problems in real time imposes 8230 The post Neural Network for Solving and Generating University Level Mathe...


Keywords: statistic, neural network, visual, network
[R] Pushing the Limits of Self-Supervised ResNets: DeepMinds ReLICv2 Beats Strong Supervised Baselines on...
https://www.reddit.com/r/artificial/ | Yesterday
Content:
nan...


Keywords: self-supervised, deepmind, framework, resnet, tpu
NVIDIA researchers' landmark achievement in machine learning uses multiresolution hash encoding
https://www.dpreview.com/ | Today
Content:
Researchers from NVIDIA have developed method for very quickly training neural graphics primitives using single GPU. Neural graphic primitives have traditionally required multiple, fully connected neural networks and are challenging, time consuming and expensive to train and evaluate.The research team, comprised of Thomas Mller, Alex Evans, Christoph Schied, and Alexander Keller, has created new input encoding method that significantly reduces the number of floating point and memory access...


Keywords: network, mathematic, metric, coding, gpu
Investigating object compositionality in Generative Adversarial Networks
https://research.google/ | Today
Content:
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, bias to account for the compositional way in which humans structure visual scene in terms of objects has......


Keywords: generative model, network, visual
An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentatio...
https://pythonawesome.com/ | Today
Content:
BaSSLThis is an official PyTorch Implementation of Boundary aware Self supervised Learning for Video Scene Segmentation BaSSL arxiv The method is self supervised learning algorithm that learns model to capture contextual transition across boundaries during the pre training stage. To be specific, the method leverages pseudo boundaries and proposes three novel boundary aware pretext tasks effective in maximizing intra scene similarity and minimizing inter scene similarity, thus leading to...


Keywords: self-supervised, network, sampling, node, test
What is Keras?
https://www.educba.com/ | Yesterday
Content:
What is Keras Keras is high level open source library for the neural network, built with Python, which can be run on Theano, CNTK, or TensorFlow. It has been created by Franco is Chollet, one of Google 8217 s engineers. It is extensible, user friendly, and scalable for faster neural network experimentation. It does support CNN 8217 s individually as well 8230 The post What is Keras appeared first on EDUCBA....


Keywords: deep learning, excel, python library
Pushing the Limits of Self-Supervised ResNets: DeepMind's ReLICv2 Beats Strong Supervised Baselines on Im...
syncedreview.com | Yesterday
Content:
Self-supervised methods in representation learning with residual networks (ResNets) have made strong progress in recent years, but still trail state-of-the-art supervised learning performance on ImageNet classification benchmarks. As the availability of unlabelled data increases and labelled data becomes more expensive and impractical to obtain, machine learning researchers believe it is crucial to advance unsupervised (or self-supervised) model training techniques. In the paper Pushing the Limits of Self-supervised ResNets: Can We Outperform Supervised Learning Without Labels on ImageNet?, a DeepMind research team proposes ReLICv2, which advances the Representation Learning via Invariant Causal Mechanisms (ReLIC) framework with better point selection strategies and demonstrates for the first time that representations learned without labels can consistently outperform a strong, supervised baseline on ImageNet and even achieve results comparable to state-of-the-art self-supervised vision transformers (ViTs)....


Keywords: transformer, self-supervised, resnet, machine learning,
Title
Univariate Time Series With Stacked LSTM, BiLSTM, and NeuralProphet
https://pub.towardsai.net/ | Yesterday   

Keywords: time series, regression, lstm, algorithms   

One more approach to optimize neural networks
https://towardsdatascience.com/ | Yesterday   

Keywords: hive, design, neural network, pytorch   

A small framework mimics PyTorch using CuPy or NumPy
https://pythonawesome.com/ | Yesterday   

Keywords: dl , lstm, python, pytorch   

Pushing the Limits of Self-Supervised ResNets: DeepMinds ReLICv2 Beats Strong Supervised Baselines on ImageNet
https://syncedreview.com | Yesterday   

Keywords: transformer, contrastive, self-supervised, resnet, machine   

Explainable Defect Detection Using Convolutional Neural Networks: Case Study
https://pub.towardsai.net/ | Yesterday   

Keywords: tpu, pre-trained, deep learning, tutorial   

Artificial Neural Network in Laymans Terms
https://towardsdatascience.com?source=rss----7f60cf5620c9---4 | Today   

Keywords: design, network, data science, classification   

Predicting future values with RNN, LSTM, and GRU using PyTorch
medium.com | Yesterday   

Keywords: network, lstm, neural network, pytorch   

Transfer Learning using EfficientNet PyTorch
https://debuggercafe.com/ | Yesterday   

Keywords: transfer learning, pytorch   

A Neural Network for Solving and Generating University Level Mathematics Problems Using Program Synthesis
https://www.marktechpost.com/ | Yesterday   

Keywords: statistic, neural network, visual, network   

[R] Pushing the Limits of Self-Supervised ResNets: DeepMinds ReLICv2 Beats Strong Supervised Baselines on...
https://www.reddit.com/r/artificial/ | Yesterday   

Keywords: self-supervised, deepmind, framework, resnet, tpu   

NVIDIA researchers' landmark achievement in machine learning uses multiresolution hash encoding
https://www.dpreview.com/ | Today   

Keywords: network, mathematic, metric, coding, gpu   

Investigating object compositionality in Generative Adversarial Networks
https://research.google/ | Today   

Keywords: generative model, network, visual   

An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentatio...
https://pythonawesome.com/ | Today   

Keywords: self-supervised, network, sampling, node, test   

What is Keras?
https://www.educba.com/ | Yesterday   

Keywords: deep learning, excel, python library   

Pushing the Limits of Self-Supervised ResNets: DeepMind's ReLICv2 Beats Strong Supervised Baselines on Im...
syncedreview.com | Yesterday   

Keywords: transformer, self-supervised, resnet, machine learning,   


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