In this article, I will describe and show the code for 4 different Pytorch training tricks that I personally have found to improve the training of my deep learning model. In a regular training loop, PyTorch stores all float variables in 32-bit precision. For people who are training their models with strict constraints, sometimes, this can cause their model to take up too much memory, forcing them to have a slower training process with a smaller model and a smaller batch size. However, storing all the variables/numbers in the model in 16-bit precision can improve upon and fix most of these problems, like dramatically decreasing the memory consumption of the model and speeding up the training loop while still maintaining the same performance/accuracy of the model. Converting all calculations to 16-bit precision in Pytorch is very simple to do and only requires a few lines of code.

Researchers from the University of Waterloo in Canada are working on developing prosthetic legs equipped with computer vision and deep-learning AI. The technologies thus employed will help legs function similar to an able-bodied person and adjust their motion by observing the surroundings. This is a case of yet another AI-powered wearable innovation with the potential to bring cheers for many. According to a report, the AI wearable market is expected to grow at a CAGR of around 30% and expected to reach a valuation of more than $180 billion by 2025. Further, the Ministry of Electronics and IT (MeitY) is soon planning to extend the PLI scheme for smart wearables, IoT, and VR products.

Yes, these are amazing places. I'm sure you've used one at least once. Yet, while a few types of media are clearly edited, different changes might be harder to spot. You may have heard the term "deepfake videos" recently. It originally came to fruition in 2017 to depict videos and pictures that incorporate deep learning algorithms to create videos and images that look real.

There are some tasks that traditional robots -- the rigid and metallic kind -- simply aren't cut out for. Soft-bodied robots, on the other hand, may be able to interact with people more safely or slip into tight spaces with ease. But for robots to reliably complete their programmed duties, they need to know the whereabouts of all their body parts. MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot's body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design.

Machine Learning and the Internet of Things (IoT) have been the buzzwords for the decade. These technologies find application in almost all industries, from enabling artificially intelligent powered digital assistants to the supply chain's automation. They have revolutionized not only how we interact on social media but also how we pay the bills. Here is how to use Machine Learning for IoT Analysis. Looking at the google trends analysis below, one can be sure that these technologies offer a lucrative career, so many people are interested in learning about them.

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. First, we initialize the libraries we will need. This will allow us to code both with pytorch and with the numpy library so that we can compare their results later.

In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug design. Yet, these methods face the problem of data scarcity while designing drugs against a novel target. In this work, the potential of deep learning and molecular modeling approaches was leveraged to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of the homologues of the target protein were screened at the active site of the target protein to create an initial target-specific dataset.

Catalyst is a PyTorch framework developed with the intent of advancing research and development in the domain of deep learning. It enables code reusability, reproducibility and rapid experimentation so that users can conveniently create deep learning models and pipelines without writing another training loop. Catalyst framework is part of the PyTorch ecosystem – a collection of numerous tools and libraries for AI development. It is also a part of the Catalyst Ecosystem – an MLOps ecosystem that expedites training, analysis and deployment of deep learning experiments through Catalyst, Alchemy and Reaction frameworks respectively. We have used the well-known MNIST dataset having 10 output classes (for classifying images of handwritten digits from 0 to 9).

Today was a fun and exciting day at the Esri Federal GIS Conference 2021 highlighted by great user presentations, inspiring talks, and a powerful technology showcase. The imagery and remote sensing demonstration showed how AI was effectively put to use in a SAAS environment. Driving the AI was a pre-trained model that is downloadable for all users from ArcGIS Living Atlas. This is just one of the many models that have been released on ArcGIS Living Atlas of the World. Ever since the pre-trained geospatial deep learning models were released on ArcGIS Living Atlas, they have been well received.

Deep learning learns through an artificial network that acts like the human brain and enables the machine to analyze data in a structured manner.