https://github.com/domus123/gpopy

GPOPY is a tool that i’m creating that use genetic algorithm for running your ML models, mathematics functions or any other type of function and computate the best parameters for you, based on a score.

I’m just finishing writing the paper that will integrate GPOPY, and in the papers i’m adding more optimization algorithms to work with GA.

It’s just an early version, but i already use it to optimize some parameters in the work and have an increase in accuracy.

Later in the future i’ll add some charts and information.

ps: Since it’s in early dev, may have some bugs and may be limited at moment.

Hi,

I am looking for a keypoint datasets for objects.

I know there are a couple of datasets for human, foot, and face. But, is there any dataset for objects that you know?

Human: http://cocodataset.org/#keypoints-2018

Foot: https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset/

Face: https://towardsdatascience.com/facial-keypoint-detection-detect-relevant-features-of-face-in-a-go-using-cnn-your-own-dataset-e09cf359c2bc

I just find the vehicle keypoint dataset;

Example Dataset: http://www.cs.cmu.edu/~mvo/index_files/Papers/CarFusion.pdf

I am looking more of this type of dataset.

Thanks in advance…

Have a nice day! I am not so experienced at ML, but at MS. So it is hard to say for me, but I suppose that future science is about great experimental skills or marvellous insights (in math or physics). Or it’s just panic and my work is not so easily algorithmized? I think both practical science and ML will enforce each other. What are your thoughts?

https://www.vice.com/amp/en_in/article/neagpb/ai-trained-on-old-scientific-papers-makes-discoveries-humans-missed

If this is too elementary and you’d like me to bother the r/learnmachinelearning people, just let me know.

We have a binary classification problem, with a dataset of size N ~ 100, and p ~ 50 features. Some of the features are categorical, thus we one-hot encode them to binary columns using sklearn.preprocessing.OneHotEncoder, which increases p unreasonably (since a categorical variable with 10 levels is expanded to 10 columns). Building a random forest or a XGBoost classifier, using a subset of the features chosen by a subject matter expert (SME), works quite well on this dataset, where with “works quite well” I mean “it does significantly better than predicting the majority class or using logistic regression”.

Now, instead than leaving the feature selection to the SME, the data scientist who’s working on this project would like to perform “proper” feature selection because some features look highly correlated (and thus the feature importance measures generated by the random forests are unreliable). How do you do feature selection when you 1) have both continuous and categorical variables with many levels, and 2) you are using a non-additive model such as random forests or XGBoost? If this was a generalized linear models, then it would be straightforward to perform feature selection by just introducing L_1 or L_2 regularization. However, I’m not sure how to do this in a principled way when using nonlinear, non-additive models such as random forests & XGBoost.

https://medium.com/ai%C2%B3-theory-practice-business/new-ocr-dataset-for-mobile-devices-716e63967b4

We introduce the Brno Mobile OCR Dataset (BMOD) for document Optical Character Recognition from low-quality images captured by handheld devices. While OCR of high-quality scanned documents is a mature field where many commercial tools are available, and large datasets of text in the wild exist, no existing datasets can be used to develop and test document OCR methods robust to non-uniform lighting, image blur, strong noise, built-in denoising, sharpening, compression and other artifacts present in many photographs from mobile devices.

This dataset contains 2 113 unique pages from random scientific papers, which were photographed by multiple people using 23 different mobile devices. The resulting 19 725 photographs of various visual quality are accompanied by precise positions and text annotations of 500k text lines. We further provide an evaluation methodology, including an evaluation server and a test set with non-public annotations. We provide a state-of-the-art text recognition baseline build on convolutional and recurrent neural networks trained with Connectionist Temporal Classification loss. This baseline achieves 2 %, 22 % and 73 % word error rates on easy, medium and hard parts of the dataset, respectively, confirming that the dataset is challenging.

I’ve been working on a project lately to predict hotel cancellations using machine learning. Hotel cancellations are a significant pain point for the industry – the inherent unpredictability of cancellations leads to lost revenue and inefficient pricing of hotel rooms.

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Using a public dataset for Portuguese hotels, I used SVM and ARIMA to 1) predict whether a customer would cancel based on select features (or customer attributes), and 2) determine the weekly fluctuations in cancellations. The latter involved significant data manipulation in pandas to structure all the data in weekly format, and ultimately the SVM demonstrated an AUC of 0.74, while SARIMA demonstrated an MDA (mean directional accuracy) of 81%.

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You can find the write-up and repository here. Would be grateful for any feedback!

As we know, Deep Learning faces certain issues (e.g., generalizability, data hunger, etc.). If we want to speculate, which controversial theories do you have in your sights you think that it is worth to look nowadays?

So far, I’ve come across 3 interesting ones:

1. Cognitive science approach by Tenenbaum: Building machines that learn and think like people. It portrays the problem as an architecture problem.
2. Capsule Networks by Hinton: Transforming Autoencoders. More generalizable DL.
3. Neuroscience approach by Hawkins: The Thousand Brains Theory. Inspired by the neocortex.

What are your thoughts about those 3 theories or do you have other theories that catch your attention?