Applications of Deep Learning

1. Colorization of Black and White Images: Deep learning can be used to use the objects and their context within the photograph in color the image like a human operator might approach the problem. The approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.

2. Machine Translation: Text translation can performed without any pre-processing 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.

3. 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, results on the ImageNet Classification problem called AlexNet.

4. 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.

5. 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. It is the breakthrough that DeepMind (now part of Google) is renown for achieving.

6. Generative Model Chatbots: A sequence to sequence based model was used to create a chatbot which learned to generate it’s own answers when trained on a lot of real live conversational datasets.