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.

Freedom of speech is a universal human right that is upheld in most countries around the world. It may be required of you to write an essay about this universal freedom as your college paper assignment. For such papers, you can access a wide variety of free research material online.

For such essays, always focus give substantial information that will educate the reader. You may decide to write an importance of freedom of speech essay for your college paper. Any topic you choose must rotate around the main subject; freedom of speech.

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;

Chinese

English

Russian

German

Czech

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.

10.Ballroom

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.