AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
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Sep 29, 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
Build your neural network easy and fast
Statistical Machine Intelligence & Learning Engine
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Tensorflow tutorial from basic to hard
Right now we don't have implementation of copy and move constructors in layer which is causing memory issues.
Follow #2314 to see the memory issues we were facing.
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.
Owl - OCaml Scientific and Engineering Computing @ http://ocaml.xyz
A Julia machine learning framework
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
Tribuo - A Java machine learning library
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
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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)))