TF-Watcher VS d2l-en

Compare TF-Watcher vs d2l-en and see what are their differences.

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. (by d2l-ai)
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TF-Watcher d2l-en
3 6
59 21,628
- 3.1%
0.0 8.7
11 months ago about 1 month ago
Python Python
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

TF-Watcher

Posts with mentions or reviews of TF-Watcher. We have used some of these posts to build our list of alternatives and similar projects.

d2l-en

Posts with mentions or reviews of d2l-en. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-10.

What are some alternatives?

When comparing TF-Watcher and d2l-en you can also consider the following projects:

EasyMSE - A Python package for generating custom Magic The Gathering cards

Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images

bibaandboba - Python package for analyzing Telegram chats and finding correlations between people

DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

hyperglass - hyperglass is the network looking glass that tries to make the internet better.

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

pypi-github-sync - Upload your Python package to PyPI with latest Github version tag.

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.

jina - ☁️ Build multimodal AI applications with cloud-native stack

einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)