Skip to content
#

Machine learning

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

Here are 53,671 public repositories matching this topic...

realiti4
realiti4 commented Oct 15, 2020

Hi, I've moved to Cuda 11.0 and I am getting warnings below while compiling. I just can't remember exactly, but I don't think I saw these with 10.2. I don't know if it related, but run_test.py is also failing when it comes to Distribution related tests. I can post test's log when the build is complete, if it is helpful. Thank you.

[3950/5005] Building NVCC (Device) object caffe2/CMakeF
bthirion
bthirion commented Dec 3, 2020

Describe the bug

In grid_to_graph, you expect the vertices to correspond to the implicit order defined by the mask. This is not always the case, due to the occurrence of isolated vertices that are dismissed in the reindexing of the vertices.

Steps/Code to Reproduce

import numpy as np
from sklearn.feature_extraction import grid_to_graph

mask = np.zeros((2, 3)).ast
trivialfis
trivialfis commented Dec 13, 2020

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.

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.

  • Updated Dec 21, 2020
  • Python
Wikipedia
Wikipedia
You can’t perform that action at this time.