Also, since I happen to have two identical webcams lying around, I adapted the code to also handle concurrent stereoscopic video feeds for the fun of it. It wasn't really meant to be a super rigorous implementation (ie, I used lstm instead of cnn layers), but I still think it had some mildly interesting outcomes. Also, imho the video feed of the loss makes for some fun visual effects.

Reinforcement learning is one of the most popular areas of research in deep learning nowadays. Part of the popularity of reinforcement learning is due to the fact that is one of the learning methods that resembles human cognition the closets. In reinforcement learning scenarios and agent learns organically by taking actions on an environment and receiving specific rewards. A little less known discipline called multi-agent reinforcement learning(MARL) focuses on reinforcement learning scenarios involving a large number of agents. Typically, MARL scenarios suffer from a scalability challenges in which its complexity increases linearly with the number of agents in the environment.

Here is a link to the video instead. "Give me jeans not shoes." That may seem like a simple request, but when we process that bit of text with our human brains, we take a lot for granted. The text is loaded with context that we parse effortlessly, thanks to a lifetime of language training (and some innate skills). Yet, it is surprisingly tricky to train a machine to do this.

To be a real "full-stack" data scientist, or what many bloggers and employers call a "unicorn," you've to master every step of the data science process -- all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. Data science students usually learn modeling skills with processed and cleaned data in text files stored on their laptop, ignoring how the data sausage is made. Students often don't realize that in industry settings, getting the raw data from various sources to be ready for modeling is usually 80% of the work. And because enterprise projects usually involve a massive amount of data that their local machine is not equipped to handle, the entire modeling process often takes place in the cloud, with most of the applications and databases hosted on servers in data centers elsewhere.

We see this daily -- smartphones recognizing faces in the camera; the ability to search particular photos with Google Images; scanning text from barcodes or book. All of this is possible thanks to the convolutional neural network (CNN), a specific type of neural network also known as strong convnet /strong . Modern deep-learning frameworks like Tensorflow and PyTorch make it easy to teach machines about images, however, there are still questions: How does data pass through artificial layers of a neural network? How can a computer learn from it? One way to better explain a convolutional neural network is to use PyTorch.

So how does this all work? Okay - let's start here. Right now you have a digital twin for every photograph ever taken of you in the past, or that will ever be taken of you in the future. These twins exists in a near-infinite matrix of possible faces (called latent space) produced by a deep-learning algorithm developed by Nvidia called StyleGAN. Like all GANs (generative adversarial networks), StyleGAN is comprised of two neural networks: a generator and a discriminator.

Evaluation was performed on a held-out test set using the standard protocol outlined by Everingham et al. (39). The precision/recall curve was computed from a method's ranked output. Recall was defined as the proportion of all positive examples ranked above a given rank, while precision is the proportion of all examples above that rank which are from the positive class. For the purpose of our task, high recall was more important than high precision (i.e., false positives are less dangerous than false negatives) to ensure no chimpanzee face detections were missed. Some false positives, such as the recognition of chimpanzee behinds as faces (e.g., fig.

The human brain has been the main inspiration for artificial intelligence (AI) researchers for decades. The quest for recreating cognitive capabilities of the brain in deep neural networks remains one of the elusive goals of AI. From that perspective, the intersection of AI and neuroscience could be considered one of the most fascinating areas of research in modern science. However, most of the current work in neural networks has been constrained to emulate the synaptic connections between neurons in the different cortex of the brain. As neural network evolves, other human cognitive skills that are becoming increasingly relevant.

Pytorch implementation of the paper Class-Balanced Loss Based on Effective Number of Samples presented at CVPR'19.

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to "read." To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts. In the art investigation domain, increasing use of extremely high-resolution digital imaging techniques is being made in parallel with the widespread adoption of a range of recent imaging and analytical modalities not previously applied in the field (e.g., hyperspectral imaging, macro x-ray fluorescence scanning, and novel forms of imaging x-ray radiography) (1–3). These techniques mean that there is a wealth of digital data available within the sector, offering huge scope to provide new insights but also presenting new computational challenges to the domain (4). In the past decades, various other disciplines, experiencing similar data growth, have benefited greatly from recent breakthroughs in artificial intelligence.