create-tf-app
d2l-en
| create-tf-app | d2l-en | |
|---|---|---|
| 3 | 6 | |
| 4 | 22,108 | |
| - | 1.8% | |
| 10.0 | 8.0 | |
| over 1 year ago | 7 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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create-tf-app
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[P] create-tf-app: Set up and maintain a machine learning project with a single script.
Check it out on: https://github.com/radi-cho/create-tf-app/ I am open to feedback and discussions. Contributions are also appreciated.
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create-tf-app: TensorFlow template + shell script to manage environments and initialize projects
Hello, I am currently setting up a simple TensorFlow template + shell script to manage environments and initialize projects. WIP: https://github.com/radi-cho/create-tf-app. I wanted to survey the community on whether such a tool would be useful and if you can provide feedback on the implementation:)
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?
Keras - Deep Learning for humans
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
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.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
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.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)