AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
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Oct 27, 2020 - Python
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AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Statistical Machine Intelligence & Learning Engine
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
It would be nice to have some time series methods. Like
Since many time series come in groups it may be useful to think about how the data should be organized to take in many time series and account for seasonality. Also prob
simple statistics for node & browser javascript
Math.NET Numerics
Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Machine Learning in R
ThunderSVM: A Fast SVM Library on GPUs and CPUs
《深度学习与计算机视觉》配套代码
MLBox is a powerful Automated Machine Learning python library.
String representations of dataset objects are used for previewing their contents from the terminal. When converting a Dataset object to a string, we build a table using ascii characters. The current table has fixed width columns that do not take full advantage of the terminal real estate if the dataset only contains a few columns.
echo $dataset;<img width="574" alt="Annotation
A Julia machine learning framework
Owl - OCaml Scientific and Engineering Computing @ http://ocaml.xyz
Tribuo - A Java machine learning library
Hi @JavierAntoran @stratisMarkou,
First of all, thanks for making all of this code available - it's been great to look through!
Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost
[CVPR19] FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation from a Single Image
mlr3: Machine Learning in R - next generation
Add linear models including instrumental variable and panel data models that are missing from statsmodels.
The bookdown version lives here: https://bookdown.org/content/3890
Add a description, image, and links to the regression topic page so that developers can more easily learn about it.
To associate your repository with the regression topic, visit your repo's landing page and select "manage topics."
Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))