8 Inspirational Applications of Deep Learning

It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. A fact, but also hyperbole.

There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech.

I try hard to convince friends, colleagues and students to get started in deep learning and bold statements like the above are not enough. It requires stories, pictures and research papers.

In this post you will discover amazing and recent applications of deep learning that will inspire you to get started in deep learning.

Getting started in deep learning does not have to mean go and study the equations for the next 2-3 years, it could mean download Keras and start running your first model in 5 minutes flat. Start applied deep learning. Build things. Get excited and turn it into code and systems.

I have been wanting to write this post for a while. Let’s get started.

Inspirational Applications of Deep Learning

Inspirational Applications of Deep Learning
Photo by Nick Kenrick, some rights reserved.


Below is the list of the specific examples we are going to look at in this post.

Not all of the examples are technology that is ready for prime time, but guaranteed, they are all examples that will get you excited.

Some are examples that seem ho hum if you have been around the field for a while. In the broader context, they are not ho hum. Not at all.

Frankly, to an old AI hacker like me, some of these examples are a slap in the face. Problems that I simply did not think we could tackle for decades, if at all.

I’ve focused on visual examples because we can look at screenshots and videos to immediately get an idea of what the algorithm is doing, but there are just as many if not more examples in natural language with text and audio data that are not listed.

Here’s the list:

  1. Colorization of Black and White Images.
  2. Adding Sounds To Silent Movies.
  3. Automatic Machine Translation.
  4. Object Classification in Photographs.
  5. Automatic Handwriting Generation.
  6. Character Text Generation.
  7. Image Caption Generation.
  8. Automatic Game Playing.

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1. Automatic Colorization of Black and White Images

Image colorization is the problem of adding color to black and white photographs.

Traditionally this was done by hand with human effort because it is such a difficult task.

Deep learning can be used to use the objects and their context within the photograph to color the image, much like a human operator might approach the problem.

A visual and highly impressive feat.

This capability leverages of the high quality and very large convolutional neural networks trained for ImageNet and co-opted for the problem of image colorization.

Generally the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.

Colorization of Black and White Photographs

Colorization of Black and White Photographs
Image taken from Richard Zhang, Phillip Isola and Alexei A. Efros.

Impressively, the same approach can be used to colorize still frames of black and white movies

Further Reading


2. Automatically Adding Sounds To Silent Movies

In this task the system must synthesize sounds to match a silent video.

The system is trained using 1000 examples of video with sound of a drum stick striking different surfaces and creating different sounds. A deep learning model associates the video frames with a database of pre-rerecorded sounds in order to select a sound to play that best matches what is happening in the scene.

The system was then evaluated using a turing-test like setup where humans had to determine which video had the real or the fake (synthesized) sounds.

A very cool application of both convolutional neural networks and LSTM recurrent neural networks.

Further Reading


3. Automatic Machine Translation

This is a task where given words, phrase or sentence in one language, automatically translate it into another language.

Automatic machine translation has been around for a long time, but deep learning is achieving top results in two specific areas:

  • Automatic Translation of Text.
  • Automatic Translation of Images.

Text translation can be performed without any preprocessing of the sequence, allowing the algorithm to learn the dependencies between words and their mapping to a new language. Stacked networks of large LSTM recurrent neural networks are used to perform this translation.

As you would expect, convolutional neural networks are used to identify images that have letters and where the letters are in the scene. Once identified, they can be turned into text, translated and the image recreated with the translated text. This is often called instant visual translation.

Instant Visual Translation

Instant Visual Translation
Example of instant visual translation, taken from the Google Blog.

Further Reading

It’s hard to find good resources for this example, if you know any, can you leave a comment.


4. Object Classification and Detection in Photographs

This task requires the classification of objects within a photograph as one of a set of previously known objects.

State-of-the-art results have been achieved on benchmark examples of this problem using very large convolutional neural networks. A breakthrough in this problem by Alex Krizhevsky et al. results on the ImageNet classification problem called AlexNet.

Example of Object Classification

Example of Object Classification
Taken from ImageNet Classification with Deep Convolutional Neural Networks

A more complex variation of this task called object detection involves specifically identifying one or more objects within the scene of the photograph and drawing a box around them.

Automatic Object Detection

Example of Object Detection within Photogaphs
Taken from the Google Blog.

Further Reading


5. Automatic Handwriting Generation

This is a task where given a corpus of handwriting examples, generate new handwriting for a given word or phrase.

The handwriting is provided as a sequence of coordinates used by a pen when the handwriting samples were created. From this corpus the relationship between the pen movement and the letters is learned and new examples can be generated ad hoc.

What is fascinating is that different styles can be learned and then mimicked. I would love to see this work combined with some forensic hand writing analysis expertise.

Sample of Automatic Handwriting Generation

Sample of Automatic Handwriting Generation

Further Reading


6. Automatic Text Generation

This is an interesting task, where a corpus of text is learned and from this model new text is generated, word-by-word or character-by-character.

The model is capable of learning how to spell, punctuate, form sentiences and even capture the style of the text in the corpus.

Large recurrent neural networks are used to learn the relationship between items in the sequences of input strings and then generate text. More recently LSTM recurrent neural networks are demonstrating great success on this problem using a character-based model, generating one character at time.

Andrej Karpathy provides many examples in his popular blog post on the topic including:

  • Paul Graham essays
  • Shakespeare
  • Wikipedia articles (including the markup)
  • Algebraic Geometry (with LaTeX markup)
  • Linux Source Code
  • Baby Names
Automatic Text Generation Example of Shakespeare

Automatic Text Generation Example of Shakespeare
Example taken from Andrej Karpathy blog post

Further Reading


7. Automatic Image Caption Generation

Automatic image captioning is the task where given an image the system must generate a caption that describes the contents of the image.

In 2014, there were an explosion of deep learning algorithms achieving very impressive results on this problem, leveraging the work from top models for object classification and object detection in photographs.

Once you can detect objects in photographs and generate labels for those objects, you can see that the next step is to turn those labels into a coherent sentence description.

This is one of those results that knocked my socks off and still does. Very impressive indeed.

Generally, the systems involve the use of very large convolutional neural networks for the object detection in the photographs and then a recurrent neural network like an LSTM to turn the labels into a coherent sentence.

Automatic Image Caption Generation

Automatic Image Caption Generation
Sample taken from Andrej Karpathy, Li Fei-Fei

These techniques have also been expanded to automatically caption video.

Further Reading


8. Automatic Game Playing

This is a task where a model learns how to play a computer game based only on the pixels on the screen.

This very difficult task is the domain of deep reinforcement models and is the breakthrough that DeepMind (now part of google) is renown for achieving.

This work was expanded and culminated in Google DeepMind’s AlphaGo that beat the world master at the game Go.

Further Reading


Additional Examples

Below are some additional examples to those listed above.

Automatically Create Styled Image From Sketch

Automatically Create Styled Image From Sketch
Image take from NeuralDoodle

More Resources

There are a lot of great resources, talks and more to help you get excited about the capabilities and potential for deep learning.

Below are a few additional resources to help get you excited.


In this post you have discovered 8 applications of deep learning that are intended to inspire you.

This show rather than tell approach is expect to cut through the hyperbole and give you a clearer idea of the current and future capabilities of deep learning technology.

Do you know of any inspirational examples of deep learning not listed here? Let me know in the comments.

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34 Responses to 8 Inspirational Applications of Deep Learning

  1. Nader September 10, 2016 at 1:13 am #

    Fantastic !!

  2. Saty September 11, 2016 at 1:22 pm #

    Hi Jason, lovely examples, great links 🙂 This is an awesome post. Thank you!

  3. vijay September 19, 2016 at 2:51 am #

    Hi Jason, Nice article.

    lately there has been lots of talk of deep learning applied to create tools which can generate
    requirements – designs – software code – create builds – test builds as well help with deploying builds to various environments.

    Is it really possible to map creative functionality of human brain with ml?

    • Jason Brownlee September 19, 2016 at 7:45 am #

      Interesting, I have not seen that.

      I’m not sure about mapping creative functions of the brain, but deep learning and other AI methods can be creative (stochastic within the bounds of what we think as aesthetically pleasing).

  4. Arthur October 2, 2016 at 9:10 am #

    Thank you for the examples. I found the automatic colarization so remarkable that I might start working in a project with it.

  5. Rodolphe October 27, 2016 at 1:01 am #

    Very nice and useful article, thanks a lot

  6. charan gudla November 8, 2016 at 4:45 am #

    You know what Jason Brownlee, I started mt PhD this year in Aug. I was taking stress on myself to find a good path for research. I somehow figured out and decided to work on deep learning, after lot of searches in internet I found your post which cleared my stress clouds in my brain. Thank you so much Jason 🙂

    Charan Gudla

    • Jason Brownlee November 8, 2016 at 9:59 am #

      Hang in there Charan Gudla, let me know how you go with your research.

  7. Farhad December 1, 2016 at 7:18 am #

    Thank you. This post is among the best posts on deep learning applications and abilities.

  8. Satis December 29, 2016 at 12:49 pm #

    Very informative . Thx.

  9. Mustafa January 16, 2017 at 5:47 pm #

    Many thanks dear prof.
    Could you please add codes for these applications

    • Jason Brownlee January 17, 2017 at 7:36 am #

      Hi Mustafa, great idea! Many of these projects are academic and the code is open source.

      Perhaps you could help to track down the github repositories?

  10. hamid January 31, 2017 at 4:19 am #

    Hi dear jason
    Tnx for great article, i have a question that how can i use deep learning for recommender system?

    • Jason Brownlee February 1, 2017 at 10:29 am #

      Hi hamid, I don’t have an example of deep learning for recommender systems.

      I don’t see why you couldn’t slot a deep learning algorithm in for a model of item-based or user-based collaborative filtering.

  11. Bernard February 9, 2017 at 1:46 am #

    Hey Jason,

    Just a quick question, I noticed that the examples provided are more geared towards the aspects of image and audio applications. Just wondering if it deep learning is just as applicable in traditional areas such as business data analysis?


    • Jason Brownlee February 9, 2017 at 7:27 am #

      Deep learning is best suited to analog type data like text, images and audio.

      It can be used on standard tabular data, but you will very likely do better using xgboost or more traditional machine learning methods.

  12. Tejas Mahajan February 12, 2017 at 7:57 pm #

    Hey Jason,

    I see you have covered Automatic Image Caption generation, you could add a 9th application of automatic image generation based on the caption or rather text. It comes under the concept of generative modelling and has received many compelling results using GANS.

    Papers : https://arxiv.org/abs/1406.2661, https://arxiv.org/abs/1605.05396


  13. Christian February 23, 2017 at 3:38 am #

    Hi Jason

    There is a very nice app called Deep Art Effects that uses Deep Learning algorithms to create art. You upload a photo, choose an art style and a neural network interprets it and turns your photo into a “painting” in this particular style. A fun aspect of Deep Learning!

  14. Aruna April 1, 2017 at 1:54 pm #

    Thank you…Your blog is very interesting.. I like to do my research in deep learning… can you note me the research areas…

    • Jason Brownlee April 2, 2017 at 6:23 am #

      Thanks Aruna.

      Sorry, I am no longer an academic, my focus is industrial machine learning. My best advice is to talk to your advisor.

  15. Anthony April 14, 2017 at 8:16 pm #

    Very nice post. Do you think machine learning and time series methods are better suited to prediction/forecasting problems involving regression?
    I am talking about problems not involving vision and audio.

    • Jason Brownlee April 15, 2017 at 9:34 am #

      I’m not sure I follow your question, perhaps you can restate it?

      • Anthony April 15, 2017 at 12:45 pm #

        Are deep learning methods suited for non-vision non-audio problems?

        Say for a typical time series, do you think deep learning outperforms traditional time series and machine learning methods?

        I am talking about time series like financial time series, electricity demand etc. etc.

        • Jason Brownlee April 16, 2017 at 9:25 am #

          Deep learning can be used for a wide range of problems.

          Is deep learning state of the art for finance? I don’t know. I expect the people exploring this question are keeping findings secret for obvious reasons.

          I have seen some promising results for LSTMs for time series forecasting, but they take a lot of training.

  16. Jerry Huang April 21, 2017 at 7:21 am #

    Great thanks it really inspires me.

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