Deep Learning is a field that has seen crazy advancement in the past couple of years. These advancements have been made possible by the amazing projects in this area, and the need for data scientists and AI engineers are high in demand.
And this surge is due to the large amount of data we collect. So in this Article, I’ll discuss some of the top deep learning projects.
1. Lung Cancer Detection Projects
Lung cancer has been one of the most difficult forms of disease to diagnose, Doctors are using their eyes for detection But nodules are harder to spot. And as a result, the cancer is either detected too late or not detected at all.
12 Sigma is An amazing company uses deep learning to train an AI algorithm that would help doctors analyze the city scan of around 500 images of one particular lung more efficiently.
Now they train the models on the GPU powered neural networks that runs 50 times faster than those running on the CPUs, And then again, providing a very good lead.
And hospitals using this models can get the result in under 10 minutes, which saves at least four to five hours of doctor’s work.
Now this is a major step forward in the deep learning or the AI industry. And I’m sure a lot of these projects help us to get better results in the future.
2. Detectorn Projects
Detectorn As I’m sure a lot of you might have heard is the Facebook’s AI research software system that implements state of the art object detection algorithms.
It is written in Python and powered by the caffe deep learning framework. You can detect every object in a article and that to live.
The goal of Detectorn is to provide a high quality high performance code base for object detection research.
It is designed to be flexible in order to support rapid implementation and evaluation of novel research.
Then again, it contains more than 50 pre trained models as well. It’s an amazing deep learning project. And I encourage people to go for it and check out the code base which is given in GitHub, And it’s an open source course so you guys can play along with the code.
3. WAVEGLOW Projects
Now talking about deep learning we have WAVEGLOW and deep learning is also doing major advancement in audio processing, And it’s not just generating music or classification.
WAVEGLOW is a flow based generating network for speech synthesis, which is powered by Nvidia. So WAVEGLOW combines insight from glue and wavenet.
In order to provide fast, efficient and high quality audio synthesis without the need for auto regression.
WAVEGLOW is implemented using only a single network trained using only a single cost function, which in turn maximizes the likelihood of the training data, and which makes the training procedures simple and stable.
5. Google Brain Projects
Google brain is a sub part of Google which works only on deep learning and AI, And Google brain has devised some new software that can create detailed images from tiny pixelated source images.
This is most amazing project by google till now and the engineer of google working hard on this project to improve this project.
6. opencog Projects
Today is more important than creating beneficial artificial general intelligence right with broad capabilities and human level and ultimately beyond.
Opencog is a project that aims to build an open source artificial intelligence framework, And it is an architecture for robot and virtual embodied cognition that defines a set of interactive components designed to give rise to human equivalent artificial general intelligence.
It is the world’s leading open source artificial general intelligence initiative, And this company is amazing. They’ve worked only on artificial general intelligence and deep learning.
The human brain consists of a host of subsystems carrying out particular tasks, some more specialized some more general in nature, and connected together in a manner enabling them to usually synergistically assist rather than work against each other.
That is what the opencog community is taking into, And the opencog design aims to capture the spirit of the brains architecture and dynamics without imitating the details.
I’m sure you guys might have heard of Sofia the Create AI bot, it is one of the first of its kind to possess the traits of artificial general intelligence, we are still in the narrow aspect.
But again, you know how Sofia is capable of reacting to emotions, speech that has never been seen before.
7. Deep Mimic
A long standing goal in cat animation is to combine data driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbation and environmental variation.
Deep Mimic is an example guided deep reinforcement learning of the physics space character skills. The reinforcement learning method can be adapted to learn robust control policies,
which are capable of imitating a broad range of example, motion clips, as you can see here, while also learning complex recoveries, adapting to changes in morphology and accomplishing user specified codes.
So it’s not just about action, it’s also about reaction. And that is an amazing area of reinforcement learning.
8. Image Outpainting
Just imagine you have half an image of a scene and you wanted the full scenery. But that’s what Image Outpainting can do for you.
This project is a Kairos implementation of Stanford image art painting paper, and Kairos being a very high level API, which is being used heavily with Theano and TensorFlow for imaging purposes.
It is one of its kind. And the model was trained with 3500 scraped beach data with argumentation totaling up to 10,500 images for 25 epochs.
9. IBM Watson
IBM Watson is an IBM supercomputer that combines artificial intelligence and sophisticated analytical software for optimal performance, such as a question answering machine.
Now, this supercomputer is named for the IBM’s founder, which is the Thomas J. Watson, hence IBM Watson, and it is powered by the latest innovation in machine learning and deep learning.
Watson is the open multi cloud platform that lets you automate the artificial intelligence lifecycle. It all sounds so difficult and confusing, but the applications of Watson underlying cognitive computing technologies are almost endless.
It is your imagination, which is going to be the limit. Because the device can perform text mining and complex analytics on huge volumes of unstructured data.
It can support a search engine for an expert system, but capabilities far superior to any previously existing number talk about the amazing use of IBM Watson.
It has been used in healthcare a lot and IBM Watson is supporting a lot of hospitals. All of the hospitals using IBM Watson are able to get the results fast.
And as I mentioned earlier about the 12 sigma, they are also working for the cancer detection, lung cancer, beat breast cancer detection and all of the procedures which require imaging as a task to know whether you have a particular infection or a disease.
IBM Watson is doing wonders there, You cannot imagine how much advancement it has made in the healthcare industry.
It is also used for analytics purpose, A lot of companies which need a lot of computation and complex analysis to be done text mining to be done.
They make the use of IBM Watson, Now Watson is being used by IBM partner program as a chatbot to provide the conversation for children’s toys.
That’s a very good step by IBM, and if we talk about teaching, IBM Watson is being used for several projects relating to education, Watson is being made available inside electronic textbooks to provide natural language, one to one tutoring to students on the reading material.
That’s a really amazing step forward taken by IBM Watson.
So guys with this, we come to the end of this particular article. And as you can see all of these projects out there, the products I’m going to talk about are all in the positive area of AI and deep learning.
Some people they worry about AI being the end of human civilization or AI being the end of human intelligence, or AI surpassing the human intelligence, that is not the case.
The case here is to create something which really helps us to create a better world around us. And with this, we come to an end of this particular article.
No, I hope these products are enough to get you started on the deep learning. And you can check GitHub source codes are all there and you can play around with the code, make your own new project and who knows you might be the next inventor.
So guys, if you have any questions regarding this session, please feel free to mention it in the comment section below.
Till then, thank you and happy learning. I hope you have enjoyed reading to this article.