Machine-Learning-Collection
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
| Machine-Learning-Collection | Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control | |
|---|---|---|
| 9 | 2 | |
| 6,991 | 114 | |
| - | - | |
| 3.5 | 4.5 | |
| 3 months ago | 12 months ago | |
| Python | Python | |
| MIT License | MIT License |
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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
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
- MPC with Gaussian processes for data-efficient reinforcement learning
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[OC] Visualizations of the learning of probabilistic model predictive control for reinforcement learning
Link to repositery: https://github.com/SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
What are some alternatives?
6DRepNet - Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
nodding-pigeon - Detection and classification of head gestures in videos
ProSelfLC-AT - noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
Robo-Semantic-Segmentation - Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline
ALAE - [CVPR2020] Adversarial Latent Autoencoders
Gradient-Centralization-TensorFlow - Instantly improve your training performance of TensorFlow models with just 2 lines of code!
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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/
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers