#Bayesian Optimization framework can intelligently trade off experiments w/ varying cost & fidelity. We achieve strong regret bounds as well as state-of-the-art performance on multiple real-world #datasets! Preprint: https://arxiv.org/abs/1811.00755v1


#machinelearning

✴️ @AI_Python_EN
🗣 @AI_Python_arXiv
❇️ @AI_Python

The 10 Biggest datasets of 2018

0) Open Images V4 from Google AI on April 30th Contains 15.4M bounding-boxes for 600 categories on 1.9M images.
Paper: https://lnkd.in/fm4xiUm

1) MURA from Stanford University ML Group on May 24 Radiographic image dataset
Paper: https://lnkd.in/fBy5szB

2) BDD100K from BAIR, Georgia Tech, Peking University, Uber AI
on May 30 Self-Driving Car Dataset.
Paper: https://lnkd.in/f-sYj9k

3) SQuAD 2.0 from Stanford
on June 11 QA Dataset.
Paper: https://lnkd.in/fYc6c5W

4) CoQA from Stanford on August 21 QA Dataset
Paper: https://lnkd.in/fKvuTvE

5) Spider 1.0 from Yale Univ on September 24 Cross-domain semantic parsing and text-to-SQL dataset.
Paper: https://lnkd.in/fWyR2x8

6) HototQA from Carnegie, Stanford, and Montreal on September 25 QA Dataset on Wiki
Paper: https://lnkd.in/fTtTgZt

7) Tencent ML Images from Tencent AI Lab on Oct 18 largest open-source multi-label image dataset
Paper: https://lnkd.in/ffV6VD5

8) Tencent AI Lab Embedding Corpus for Chinese words and phrases on Oct 19 Embeddings Dataset
Paper: https://lnkd.in/ffV6VD5

9) fastMRI from NYU and Facebook AI on November 26
Knee MRI Images Dataset
Paper: https://lnkd.in/fQuUDNk

Read: https://lnkd.in/fXU9Kr6

#dataset #datasets

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv

Shuffling large datasets, have you ever tried that?

Here the author presents an algorithm for shuffling large datasets.
Here you learn the following;

0. why Shuffle in the first place?
1. A 2-pass shuffle algorithm is tested
2. How to deal with oversized piles
3. Parallelization & more

Link to article : https://lnkd.in/dZ8-tyJ
Gist on #Github: for a cool visualization of the shuffle https://lnkd.in/d8iK8fd

#algorithms #github #datasets #deeplearning #machinelearning

❇️ @AI_Python
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

Deep Convolutional Sum-Product Networks for Probabilistic Image Representations

Sum-Product Networks (SPNs) are hierarchical probabilistic graphical models capable of fast and exact inference.

Applications of SPNs to real-world data such as large image datasets has been fairly limited in previous literature. Here is a Convolutional Sum-Product Networks (ConvSPNs) which exploit the inherent structure of images in a way similar to deep convolutional neural networks, optionally with weight sharing.
#neuralnetworks #datasets #deeplearning

Paper: https://lnkd.in/ei4Gqjy

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

It is time we shared the dataset with everyone. This is a collection of text from Tamil news articles. Has around 7 millions lines of text, all cleaned up, ready to used for language modelling task, in case anyone want to try. You can use the code from git repo below to get started.

Dataset:
https://lnkd.in/fzg3xyM]
Code:
https://lnkd.in/fezt4M8 #datasets

✴️ @AI_Python_EN

To much spelling error in your dataset?


Peter Norvig (Research Director at Google, previously director of search quality) revolutionize search engine quality by giving power to reduce spelling error (by splits, deletes, transposes, replaces, and inserts). You can see the comprehensive guide (with python code) at his website https://lnkd.in/fEb3v2a

#python #datasets #codes #statistician

✴️ @AI_Python_EN

Not everyone knows but my #book has its Github repository where all #Python code used to build illustrations is gathered.

So, while reading the book, you can actually run the described #algorithms, play with hyperparameters and #datasets, and generate your versions of illustrations.

https://github.com/aburkov/theMLbook

✴️ @AI_Python_EN

Transition guide from Excel’s analyst to Python Programming for Data Analysis

1. From Excel to Pandas https://lnkd.in/fnU5apw
2. Communication & Data Storytelling https://lnkd.in/eqf5gUV
3. Data Manipulation with Python https://lnkd.in/g4DFNpJ
4. Data Visualization with Python (Matplotlib/Seaborn): https://lnkd.in/g_3fx_6
5. Advanced Pandas https://lnkd.in/fZWGp9B
6. Tricks on Pandas by Real Python https://lnkd.in/fXc9XSp
7. Becoming Efficient with Pandas https://lnkd.in/f64hU-Y
8. Pandas Advances Tips https://lnkd.in/fGyBc4c
9. Jupyter Notebook (Beginner) https://lnkd.in/fTFinFi
10. Jupyter Notebook (Advanced) https://lnkd.in/fFufePv

#datavisualization #python #programming #pydata #datasets #pandas #datasets

✴️ @AI_Python_EN

Tools like #PyTorch, fast.ai, and open source #datasets are making deep learning faster and more accessible. Learn how one #ML hobbyist used these resources to train a convolutional neural network that can classify gastrointestinal images.

🌎 Link

✴️ @AI_Python

25 Excellent #MachineLearning Open #Datasets

🌎 more learn


✴️ @AI_Python_EN