Machine-Learning-Collection
Amortized-SVGD-GAN
| Machine-Learning-Collection | Amortized-SVGD-GAN | |
|---|---|---|
| 9 | 1 | |
| 6,991 | 9 | |
| - | - | |
| 3.5 | 0.0 | |
| 3 months ago | over 2 years ago | |
| Python | Python | |
| 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
Amortized-SVGD-GAN
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A way to draw samples from a continuous multidimensional probability distribution using Amortized Stein Variational Gradient Descent
My Github repo: https://github.com/mokeddembillel/Amortized-SVGD-GAN
What are some alternatives?
6DRepNet - Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
nodding-pigeon - Detection and classification of head gestures in videos
PyTorch-VAE - A Collection of Variational Autoencoders (VAE) in PyTorch.
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
ALAE - [CVPR2020] Adversarial Latent Autoencoders
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
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/
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
Fuel-Consumption-Estimator - Desktop application that estimates fuel consumption using a trained machine learning model.
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments