machine_learning_examples
d2l-en
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| machine_learning_examples | d2l-en | |
|---|---|---|
| 3 | 6 | |
| 8,091 | 21,628 | |
| - | 3.1% | |
| 5.3 | 8.7 | |
| 8 days ago | about 1 month ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
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machine_learning_examples
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Doubt about numpy's eigen calculation
Does that mean that the example I found on the internet is wrong (I think it comes from a DL Course, so I'd imagine it is not wrong)? or does it mean that I am comparing two different things? I guess this has to deal with right and left eigen vectors as u/JanneJM pointed out in her comment?
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How to save an attention model for deployment/exposing to an API?
I've been following a course teaching how to make an attention model for neural machine translation, This is the file inside the repo. I know that I'll have to use certain functions to make the textual input be processed in encodings and tokens, but those functions use certain instances of the model, which I don't know if I should keep or not. If anyone can please take a look and help me out here, it'd be really really appreciated.
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?
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
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.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
neptune-client - 📘 The MLOps stack component for experiment tracking
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
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.