Deep Learning
If you ask me, What is Deep Learning? I would always say that it's mostly a way to play with the image, video or audio data. Well, that being the informal definition of Deep Learning, the formal definition for it comes like this, "Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks."
More about Deep Learning
A Brief Definition
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.
Applications of Deep Learning
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Inspiration
Artificial Neural Networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.
Mission
Since, we have talked a lot about what Deep Learning is, it's time to dive into the task we have made this repository. In order to increase awareness about Open Source among people with the Machine Learning(more specifically Deep Learning) field as their playground, we made this repository to engage Deep Learning enthusiats in contributing to this repository by simply telling us more about at least one Deep Learning model. This way, we can collect a large base of information and details about different DL models at one place, which eventually would be very helpful for people in learning about models.
How To Contribute
To contribute to this repository, what you simply need to do is create a Pull Request with the correct format of data files as told in the example model and wait for your Pull Request to be merged! To let us know about your Pull Request, simply comment your PR tag number with a message saying, "Content added".