d2l-en
einops
Our great sponsors
d2l-en | einops | |
---|---|---|
6 | 17 | |
21,628 | 7,897 | |
3.1% | - | |
8.7 | 7.4 | |
about 1 month ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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.
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.
-
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
-
The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
-
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.
einops
- Einops: Flexible and powerful tensor operations for readable and reliable code
-
Yorick is an interpreted programming language for scientific simulations
Thanks for the pointer. I can believe that a language that looks so different will find that different patterns and primitives are natural for it.
My experience from writing a lot of array-based code in NumPy/Matlab is that broadcasting absolutely has made it easier to write my code in those ecosystems. Axes of length 1 have often been in the right places already, or have been easy to insert. It's of course possible to create a big mess in any language; it seems likely that the NumPy code you saw could have been neater too.
In machine learning there can be many array dimensions floating around: batch-dims, sequence and/or channel-dims, weight matrices, and so on. It can be necessary to expand two or more dimensions, and/or line up dimensions quite carefully. Einops[1] has emerged from that community as a tool to succinctly express many operations that involve lots of array dimensions. You're likely to bump into more and more people who've used it, and again it seems there's some overlap with what Rank does. (And again, you'll see uses of Einops in the wild that are unnecessarily convoluted.)
[1] https://einops.rocks/ -- It works with all of the existing major array-based frameworks for Python (NumPy/PyTorch/Jax/etc), and the emerging array API standard for Python.
-
Torch qeuivalent to image_to_array (keras)
this is definitely what you're looking for: https://github.com/arogozhnikov/einops
-
[D] Have their been any attempts to create a programming language specifically for machine learning?
Einops all the things! https://einops.rocks/
- Delimiter-First Code
-
[D] Any independent researchers ever get published/into conferences?
It depends on what are their main purposes. I know some figures who have done an amazing job in this field but never because of publications, e.g. https://github.com/lucidrains and https://github.com/rwightman, https://einops.rocks/
-
[D] Anyone using named tensors or a tensor annotation lib productively?
On tsalib's warp: this is very similar to einops. I think it might even be slightly more general. However, I'm honestly not sure to what extent tsalib is still maintained, as it looks like the most recent commit was about two years ago. (Which is a shame.)
-
A basic introduction to NumPy's einsum
Also see Einops: https://github.com/arogozhnikov/einops, which uses a einsum-like notation for various tensor operations used in deep learning.
https://einops.rocks/pytorch-examples.html shows how it can be used to implement various neural network architectures in a more simplified manor.
-
Ask HN: What technologies greatly improve the efficiency of development?
This combined with something like einops [1] ( intuitive reshaping library) can be a huge time saver.
[1] https://github.com/arogozhnikov/einops
-
[D] What are your favorite tools to visualize/explain tensor operations?
einops: just look at the pretty visual GIF and be amazed
What are some alternatives?
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
extending-jax - Extending JAX with custom C++ and CUDA code
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
opt_einsum - ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
kymatio - Wavelet scattering transforms in Python with GPU acceleration
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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.
3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch