10 Open Datasets for Deep Learning Every Data Scientist Must Work With

10 Open Datasets for Deep Learning Every Data Scientist Must Work With

As a scientist, you can use a data set as a way to communicate with other scientists. These datasets are used for various purposes mostly related to deep learning techniques. That is why they are more common among scientists with a higher knowledge.

Open Datasets for Deep Learning Techniques
Normally, datasets for deep learning techniques are categorized based on their actual use. These three categories include;

Image processing
Natural language processing
Audio processing

Whether visual data, natural language or audio data, you must pick a suitable data set. As a scientist, you are free to choose the most suitable dataset to use for your work.
Some of the open datasets that are available to scientists include but not limited to the following;
1.    MNIST
This is an image-based data set of handwritten digits containing 60,000 training set examples and 10,000 test set examples. It is often used to analyze and recognize patterns in the real world without spending much time and effort in data preprocessing phase.
2.    MS-COCO
This is also an image processing dataset that is used for detecting, segmenting and captioning and image or object. It is one of the largest datasets that exist. Some of its key features range from;

1.5 million object instances
Object segmentation
Recognition in context
Superpixel stuff segmentation
80 object categories

Other important features include 5 captions per image and 91 stuff categories. Its total size is 25GB while compressed.
3.    ImageNet
This image processing dataset is also one of the largest with a total size of 150GB uncompressed. Its data consists of images organized according to WorldNet hierarchy
4.    Open Images Dataset
This is arguably the largest image processing data set with a total size of 500GB and a record of more than 9 million images.
5.    The Wikipedia Corpus
This is a natural language data set containing a collection of all texts on Wikipedia. With only a size of 20MB, the dataset contains a total of 1.9 billion words drawn from a total of more than 4 million excerpts.
6.    The Blog Authorship Corpus
This dataset is used to analyze natural language. It is a collection of blog posts collected from thousands of bloggers where each blog is delivered as a separate file. Its total size is 300MB and a record of more than 140 million words and more than 680,000 articles.
7.    Machine Translation of Various Languages
This data set is commonly used for translation consisting of several European languages. These languages include;


8.    LibriSpeech
It is an audio processing dataset which consists a collection of audiobooks from LibriVox project. It is a 1000 hours of speech data set with a total size of 60GB.
9.    Free Spoken Digit Dataset
This dataset helps identify spoken digits in audio samples. It is a growing data set with only 1,500 audio samples and a size of only 10MB.
Contains ballroom dancing audio files with 698instances. This dataset is 14GB in size while compressed.

These are some of the common open datasets you can use as a scientist for deep learning techniques. Without them, your research and learning process may be a little hard. You need to pick each dataset according to the kind of data you are analyzing.

Link: 10 Open Datasets for Deep Learning Every Data Scientist Must Work With