torchinfo
d2l-en
| torchinfo | d2l-en | |
|---|---|---|
| 3 | 6 | |
| 2,294 | 21,704 | |
| - | 1.3% | |
| 6.9 | 8.5 | |
| 2 days ago | 10 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
torchinfo
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[D] PyTorch and Tensorflow Performance Different on the same model, dataset and hyperparameters
It may be a good idea to compare the implementation of the models using Keras's Model.summary and PyTorch's torchinfo.
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after adding a nn.Dropout() layer, the number of parameters won't change?
No they wont change. If you are interested in viewing parameters check out Torchinfo https://github.com/TylerYep/torchinfo
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zero_grad() is supposed to be invoked every time one data point passed? How does a scalar.backward() from a loss function affect another model parameters?
You can use torchinfo, it has a # params column and it's a nice way to log and debug a NN architecture to make sure all the layers you expect are connected.
d2l-en
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
What are some alternatives?
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
deepo - Setup and customize deep learning environment in seconds.
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
torchSR - Super Resolution datasets and models in Pytorch
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.