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Examples Alternatives
Similar projects and alternatives to examples
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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aws-graviton-getting-started
Helping developers to use AWS Graviton2 and Graviton3 processors which power the 6th and 7th generation of Amazon EC2 instances (C6g[d], M6g[d], R6g[d], T4g, X2gd, C6gn, I4g, Im4gn, Is4gen, G5g, C7g[d][n], M7g[d], R7g[d]).
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pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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raccoon_dataset
The dataset is used to train my own raccoon detector and I blogged about it on Medium
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text-rnn
text-rnn allows you to create modern neural network architectures which use modern techniques such as skip-embedding and attention weighting. Train either a bidirectional or normal LSTM recurrent neural network to generate text using any dataset. You can continue training a pre-trained model.
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better examples alternative or higher similarity.
examples reviews and mentions
Posts with mentions or reviews of examples.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-22.
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A Distributed File System in Go Cut Average Metadata Memory Usage to 100 Bytes
For “cloud-native” apps, JuiceFS is not needed.
S3 is not designed for intensive metadata operations, like listing, renaming etc. For these operations, you will need a somewhat POSIX-complaint system. For example, if you want to train on ImageNet dataset, the “canonical” way [1] is to extract the images and organize them into folders, class by class. The whole dataset is discovered by directory listing. This where JuiceFS shines.
Of course, if the dataset is really massive, you will mostly end-up with in-house solutions.
[1]: https://github.com/pytorch/examples/blob/main/imagenet/extra...
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Logistic Regression for Image Classification Using OpenCV
Pytorch includes a simple neural network example for the MNIST data: https://github.com/pytorch/examples/blob/main/mnist/main.py
It only takes a few minutes to train with default parameters and will have >99% accuracy on the MNIST test set.
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[R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
pytorch-examples
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I work at a non-tech company and have been asked to make software that is impossible. How do I explain it to my boss?
Pretty much just grab one of these, swap in your own database, go home early: https://pytorch.org/examples/
- MIT Course: Generative AI for Constructive Communication
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From a Dumb Student to a PyTorch Contributor: The Impact of Teachers on My Life⚡
The cherry on top of the cake I've added my father's name at the top of the code in the comments. I hope that for the next upcoming 200-300 years, someone will read modify and improve or perform experiments with my code.(Vivek V patel), My code can be found at official PyTorch's Website https://pytorch.org/examples/(Image Classification Using Forward-Forward Algorithm)
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What modifications can maximize the efficacy of the REINFORCE algorithm for a policy gradient task?
I am straying out of my domain knowledge to attempt a basic reinforcement learning task in a toy environment and have become fairly familiar with the REINFORCE algorithm for policy gradient agents, especially PyTorch’s implementation (found here). It is clear to me now that there are superior methods to train RL agents (PPO for instance), but as I read, these feel beyond my current intellectual or time resources. As such, I’d like to eek out as much power through modifications of REINFORCE as possible before determining how I might move on.
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How does Taichi differ from PyTorch? They are different in every sense!
import torch import torch.nn as nn import torch.nn.functional as F # Simplified version of https://github.com/pytorch/examples/blob/main/mnist/main.py#L21 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) def forward(self, x): x = self.conv1(x) output = F.relu(x) return output
- Noob PyTorch Question
- Syntax Error, attempting to train neural network.
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A note from our sponsor - SaaSHub
www.saashub.com | 19 Apr 2024
Stats
Basic examples repo stats
23
21,695
7.7
3 days ago
pytorch/examples is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of examples is Python.
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