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Add this line to your application's Gemfile: It can take a few minutes to compile the extension. Deep learning is significantly faster with a GPU. If you don't have an NVIDIA GPU, we recommend using a cloud service. Paperspace has a great free plan. We've put together a Docker image to make it easy to get started.

Until recently, the study of autoencoders had primarily been an academic pursuit, said Nathan White, lead consultant at AIM Consulting. However, there are now many applications where machine learning practitioners should look to autoencoders as their tool of choice. An autoencoder consists of a pair of deep learning networks, an encoder and decoder. The encoder learns an efficient way of encoding input into a smaller dense representation, called the bottleneck layer. After training, the decoder converts this representation back to the original input.

This Data Science project intends to provide an image-based automatic inspection interface. It entails the use of self-designed image processing and deep learning methods. It will classify plant leaves as healthy or infected. There are many popular deep learning projects on the MRI scan dataset. One of them is Brain Tumor detection.

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With the slow down and shutdown of a large portion of society due to the COVID-19 virus, I took this as an opportunity to learn something new. Here are the results of my twelve week deep dive into pharmaceutical AI. On average, it takes ten years and costs $2.6 billion dollars to take a drug from the point of understanding the root cause of a disease to its availability in the marketplace. A large portion of this time and effort/cost is because we are literally looking for a needle in a haystack. We are looking for the one molecule that can turn off a disease at the molecular level in a solution space of between 10³⁰ to a google (yes, 10¹⁰⁰) synthetically feasible molecules. The chemical solution space is too vast to be efficiently screened for the particular molecule of interest.

Computer vision is a subject to convert images and videos into machine-understandable signals. With these signals, programmers can further control the behavior of the machine based on this high-level understanding. Among many computer vision tasks, image classification is one of the most fundamental ones. It not only can be used in lots of real products like Google Photo's tagging and AI content moderation but also opens a door for lots of more advanced vision tasks, such as object detection and video understanding. Due to the rapid changes in this field since the breakthrough of Deep Learning, beginners often find it too overwhelming to learn. Unlike typical software engineering subjects, there are not many great books about image classification using DCNN, and the best way to understand this field is though reading academic papers. But what papers to read? In this article, I'm going to introduce 10 best papers for beginners to read.

Image Captioning is a challenging artificial intelligence problem which refers to the process of generating textual description from an image based on the image contents. A common answer would be "A woman playing a guitar". We as humans can look at a picture and describe whatever it is in it, in an appropriate language. For all of us'non-radiologists', a common answer would be "a chest x-ray". Well, we are not wrong but a radiologist might have some different interpretations.

It is my second article on the Recommendation systems. In my previous article, I have talked about content-based and collaborative filtering systems. I will encourage you to go through the article if you have any confusion. In this article, we are going to see how Deep Learning is used in Recommender systems. We will go through the recommender system's candidate generation architecture of Youtube.

Think critically about whether you need to apply deep-learning to your datasets. Deep Learning, one of the "hottest" things in AI, has a way of seeping into popular culture as this mysterious, software that can make seemingly amazing classifications at human-level accuracy in Computer Vision, speech recognition, or play games like Go, recommend our favorite movies, and the like. But deep learning has crucial pitfalls, when it drives cars that sadly, more than once, have injured or killed their drivers or pedestrians because of silly image-recognition mistakes. Or, when deep learning is used for face-recognition ––something that clearly has adverse effects on people of color, LGBT, and other marginalized groups –– and if deep learning's face-prediction is used by institutions of power with a history of racism, LGBT-phobia, and tossed back and forth between private companies and governments –– deep-learning's pitfalls become frighteningly magnified. Another example is when Facebook's deep-learning neural translation machine led to the illegal arrest of a Palestinian man because of a post he made, at the end of 2017.