Machine-Learning-Collection
Fuel-Consumption-Estimator
Machine-Learning-Collection | Fuel-Consumption-Estimator | |
---|---|---|
9 | 18 | |
6,991 | 15 | |
- | - | |
3.5 | 10.0 | |
3 months ago | over 1 year ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Machine-Learning-Collection
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Building an AI Game Bot 🤖Using Imitation Learning and 3D Convolution ResNet
def compute_mean_std(dataloader): ''' We assume that the images of the dataloader have the same height and width source: https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_std_mean.py ''' # var[X] = E[X**2] - E[X]**2 channels_sum, channels_sqrd_sum, num_batches = 0, 0, 0 for batch_images, labels in tqdm(dataloader): # (B,H,W,C) batch_images = batch_images.permute(0,3,4,2,1) channels_sum += torch.mean(batch_images, dim=[0, 1, 2, 3]) channels_sqrd_sum += torch.mean(batch_images ** 2, dim=[0, 1, 2,3]) num_batches += 1 mean = channels_sum / num_batches std = (channels_sqrd_sum / num_batches - mean ** 2) ** 0.5 return mean, std compute_mean_std(dataloader)
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What can be the reasons of BatchNorm working and Dropout not working in YoloV1 Pytorch implementation?
I then found Aladdin Persson implementation (which he described in YouTube video). He said that original paper used Dropout, because BatchNorm was not invented at the time, and he wants to use BatchNorm instead. I thought there is no critical difference between these two, and decided to stick up with paper for the sake of learning to implement such things.
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How to create a custom parallel corpus for machine translation with recent versions of pytorch and torchtext?
I am trying to train a model for NMT on a custom dataset. I found this great tutorial on youtube along with the accompanying repo, but it uses an old version of PyTorch and torchtext. More recent versions of torchtext have removed the Field and BucketIterator classes. I looked for more recent tutorials. The closest thing I could find was this medium post (again with the accompanying code) which worked with a custom dataset for text classification. I tried to replicate the code with my problem and got this far:
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I need help knowing how to improve a CycleGan I am working on.
with the source code here: Machine-Learning-Collection/ML/Pytorch/GANs/CycleGAN at master · aladdinpersson/Machine-Learning-Collection · GitHub
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Pytorch: Custom Dataset for Machine Translation
seq2seq_attention
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Project to prettify music notes
I followed a tutorial to do a pix2pix GAN network here: https://www.youtube.com/watch?v=SuddDSqGRzg, and the github.
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Awesome Youtube
Aladdin Persson
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Help with initial set-up.
Hello everyone. I am all set up and ready to go. I downloaded the code below (btw, I don't fully understand what it does, lol) and ran it a few times.
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YOLOv3 from scratch in PyTorch
Code: https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/object_detection/YOLOv3
Fuel-Consumption-Estimator
- [P] Desktop app. makes fuel consumption estimation with machine learning.
- My desktop app that makes fuel consumption estimation with artificial intelligence. Open source github repo. Please give a star for support me.
- [P] An application that optimizes the fuel consumption of your vehicles with artificial intelligence. Open source. Please star for support.
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any decent ideas for a machine learning project using excel, r, SQL etc
The project just for you. Please star on github to support. Check out my other projects, you will definitely like it. Github link
- My desktop app that makes fuel consumption estimation with artificial intelligence. Open source github repo.
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Open source project: Application that makes fuel consumption estimation with artificial intelligence.
Please give star for support me. Github Link
- My desktop app that makes fuel consumption estimation with artificial intelligence. Open source project.
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An application that predicts fuel consumption of vehicles with artificial intelligence, aiming to save fuel. Completely Open source. You can star the github repo to support it.
The application is written entirely using python language and QT framework. The project is completely open source, come to the github repo to support you. Github repo. Click here
- Awesome artificial intelligent project. Open source repo.
- Awesome AI app. You can makes fuel consumption estimation with artificial intelligence. Please star for support.
What are some alternatives?
6DRepNet - Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
nodding-pigeon - Detection and classification of head gestures in videos
venvipy - A GUI for managing Python virtual environments
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
PyQt-React-Boilerplate - A boilerplate for using python to build a desktop application using PyQt webengine and React.js as the application front-end.
ALAE - [CVPR2020] Adversarial Latent Autoencoders
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control - Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments
WebWidget - Basic Floating translucent WebBrowser Widget App for Windows.
Gradient-Centralization-TensorFlow - Instantly improve your training performance of TensorFlow models with just 2 lines of code!
pytorch-accelerated - A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/