Deep Learning is clearly a field that has seen crazy advancements in the past couple of years. These advancements have been made possible by the amazing projects in this area. 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. Moreover, if you want to get better at Deep Learning and pursue a complete job profile into this, 

Image Enlarging

Google Brain has devised some new software that can create detailed images from tiny, pixelated source images. Google’s software, in short, basically means the “zoom in… now enhance!” TV trope is actually possible. First, take a look at the image on the right.

The left column contains the pixelated 8×8 source images, and the center column shows the images that Google Brain’s software was able to create from those source images. For comparison, the real images are shown in the right column. As you can see, the software seemingly extracts an amazing amount of detail from just 64 source pixels. It’s ana amazing Deep Learning Project.

Image Outpainting

Imagine you have a half image of a scene and you wanted the full scenery, well that’s what image outpainting can do that for you. This project is a Keras implementation of Stanford’s Image Outpainting paper. The model was trained with 3500 scrapped beach data with argumentation totaling up to 10500 images for 25 epochs.

This is an amazing paper with a detailed step by step explanation. A must-try example for all the Deep Learning Enthusiasts. Personally, this is my favorite Deep Learning project.

Lung Cancer Detection

Lung cancer has long been one of the most difficult forms of the disease to diagnose. With doctors using their eyes for detection, the nodules are harder to spot and as a result, the cancer is either detected too late or not detected at all. The nodule can have a variety of looks, and it takes doctors years to know all the different looks.

12 Sigma uses deep learning to train an AI algorithm that would help doctors analyze CT scan images more efficiently. They train the models on GPU-powered neural networks that run 50 times faster than those running CPUs. Hospitals using the Model can get results in under 10 min which saves at least 4-5 hours of Doctor’s work.