What are some of the popular data sets available for machine and deep learning?
Machine Learning Datasets for Natural Language Processing
- Enron Email Dataset. This Enron dataset is popular in natural language processing.
- The Yelp Dataset.
- Jeopardy Dataset.
- Recommender Systems Dataset.
- UCI Spambase Dataset.
- Flickr 30k Dataset.
- IMDB reviews.
- MS COCO dataset.
Can we use CNN for non image data?
Despite its huge success with image data CNN is not designed to handle non-image. (and non-time series) data. Arguably, any problem that can represent the correlation of features of a given data example in a single map, may be attempted via CNN.
Where can I get large datasets?
Sources for Finding Large Datasets
- A Guide to International and US Statistics Sources. List of major sources for datasets with descriptions and links.
- Data.gov. ‘Find, download, and use datasets that are generated and held by the Federal Government.
- HealthData.gov.
Which is the best dataset for image classification?
This dataset is another one for image classification. It consists of 60,000 images of 10 classes (each class is represented as a row in the above image). In total, there are 50,000 training images and 10,000 test images. The dataset is divided into 6 parts – 5 training batches and 1 test batch.
What can datasets be used for in deep learning?
You can use them to hone your skills, understand how to identify and structure each problem, think of unique use cases and publish your findings for everyone to see! The datasets are divided into three categories – Image Processing, Natural Language Processing, and Audio/Speech Processing. Let’s dive into it!
How to prepare image data for a neural network?
It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of image data augmentation techniques during both the training and evaluation of the model.
Which is the best dataset for object detection?
This is a real-world image dataset for developing object detection algorithms. This requires minimum data preprocessing. It is similar to the MNIST dataset mentioned in this list, but has more labelled data (over 600,000 images). The data has been collected from house numbers viewed in Google Street View.