Sometimes we don't have too much time to write and think about a structure for a medium article. Therefore, this algorithms assists us at dataalliance.io with generating a skeleton text for Medium articles, reducing time spent on the creativity aspect of writing an article.
Based on the NLP model: GPT-3 by OpenAI, funded by Elon Musk etc. Find their work at OpenAI.com.
Input: "Generate a text describing what Deep Learning is."
What is Deep Learning?
Deep learning is one of the best methods we have for trying
to solve problems that we don’t know how to solve using
traditional computer programs and algorithms (e.g.
understanding natural language, recognizing objects in
photos, driving a car).
It is called “deep” because you can make use of many
layers of computational nodes; if you’re new to all of
this, a good way to start thinking about it is like trees:
each node in the tree represents a simpler component and
the complexity is deepened from there. The nodes are
linked together hierarchically, which makes the models
easier to process than they would be otherwise.
An artificial neural network built for image recognition,
for example, may have an input layer, multiple hidden
layers and an output layer.
What are Some Examples of Deep Learning
Image recognition and processing — Early deep
learning systems were designed to recognize images,
labeling them with a confidence score. With a computer’s
help, this process would involve creating a description
that matches an example image. These approaches now
enable companies to automatically translate text-based
photos into something a human can easily understand,
even when the style or caption is extremely ambiguous.
Applications for face recognition include biometric
security. Intelligent assistants use computer vision to
provide visual assistance on your device as well as
perform image processing to recognize faces and objects
in photos.
Virtual assistants — A virtual assistant can give you
useful advice based on your previous interactions with it.
Data mining data — A collection of databases for modeling
provides users with the ability to analyze social media
behavior and organize customer feedback via product
reviews. By integrating deep learning models into advertising, products are better able to reach a desired audience based on their demographic information and online behavior (e.g., clicks, subscriptions, comments, etc.)
Automotive Autonomous vehicles use computer vision systems to identify potential obstacles and take evasive action.
What can we expect in the future from Deep Learning?
1. Higher-level, compositional forms of reasoning with
models that are structurally richer and more general than
our current deep nets.
2. Flexible learning mechanisms that allow the models
to develop general solutions to novel problems on their
own without having to be engineered by hand.
3. Automatic technology for designing new features and
architectures.