all kinds of text classification models and more with deep learning
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Updated
May 20, 2020 - Python
all kinds of text classification models and more with deep learning
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.
According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li
Statistical Machine Intelligence & Learning Engine
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
Tensorflow tutorial from basic to hard
Natural language detection
CNN-RNN中文文本分类,基于TensorFlow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Describe the bug
This behavior is present in a plethora of catalyst's callbacks and losses. It's consistent, but it's definitely confusing for many new users.
To Reproduce
Steps to reproduce the behavior:
Use these functions/classes:
Callbacks
MeterMetricsCallback](https://github.com/catalyst-team/catalyst/blob/masIn 'pointnet2/scannet/preprocessing/collect_scannet_scenes.py' line #17, we refer to 'scannet_all.txt' file. It would be helpful if you guide where to get or how to generate this file?
Sandbox for training convolutional networks for computer vision
When following the manual install guide i had to install the following additional dependencies manually:
sudo apt-get install pulseaudio swig libpulse-dev libasound2-dev
sudo pip3 install pocketsphinx spidev
After, it all works great on Raspberry Pi Zero W!
Machine Learning in R
Hi NVIDIA Team,
To make this project successful, I would like to suggest to add few things. I would love to assist on this.
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Implementation of EfficientNet model. Keras and TensorFlow Keras.
Caffe models (including classification, detection and segmentation) and deploy files for famouse networks
ThunderSVM: A Fast SVM Library on GPUs and CPUs
Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation),etc. All codes are implemented intensorflow 2.0.
MLBox is a powerful Automated Machine Learning python library.
In experiments.rst, in the first diagram, I'm inferring that ovals are static resources and parallelograms are processes, and that dashed lines means optional. But if that's true, then I'm not sure what the meaning of purple vs. blue is, the scenes should be ovals and solid lines. It might be simpler and easier to parse if everything was a blue oval, although the dashed line makes sense. In the
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
Code that accompanies my blog post outlining five video classification methods in Keras and TensorFlow
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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)))