Deep Learning experiments have critical requirements regarding the careful handling of their datasets as well as the efficient and correct usage of APIs that interact with hardware accelerators. On the one hand, software mistakes during data handling can contaminate experiments and lead to incorrect results. On the other hand, poorly coded APIs that interact with the hardware can lead to sub-optimal usage and untrustworthy conclusions. In this work we investigate the use of Linear Logic for the analysis of Deep Learning experiments. We show that primitives and operators of Linear Logic can be used to express: (i) an abstract representation of the control flow of an experiment, (ii) a set of available experimental resources, such as API calls to the underlying data-structures and hardware as well as (iii) reasoning rules about the correct consumption of resources during experiments. Our proposed model is not only lightweight but also easy to comprehend having both a symbolic and a visual component. Finally, its artifacts are themselves proofs in Linear Logic that can be readily verified by off-the-shelf reasoners.

Palimpsests refer to historical manuscripts where erased writings have been partially covered by the superimposition of a second writing. By employing imaging techniques, e.g., multispectral imaging, it becomes possible to identify features that are imperceptible to the naked eye, including faded and erased inks. When dealing with overlapping inks, Artificial Intelligence techniques can be utilized to disentangle complex nodes of overlapping letters. In this work, we propose deep learning-based semantic segmentation as a method for identifying and segmenting individual letters in overlapping characters. The experiment was conceived as a proof of concept, focusing on the palimpsests of the Ars Grammatica by Prisciano as a case study. Furthermore, caveats and prospects of our approach combined with multispectral imaging are also discussed.

Hydra library provides a flexible and efficient configuration management system that enables creating hierarchical configurations dynamically by composition and overriding through config files and the command line. This powerful tool offers a simple and efficient way to manage and organize various configurations in one place, constructing complex multilevel configs structures without any limits which can be essential in machine learning projects. All of that enables you to switch easily between any parameters and try different configurations without manually updating the code. By defining the parameters in a flexible and modular way, it becomes much easier to iterate over new ML models and compare different approaches faster, which can save time and resources, and, besides, make the development process more efficient. Hydra can serve as the central component in deep learning pipelines (you can find an example of my training pipeline template here), which would orchestrate all internal modules.

Batch Size has a massive impact on your model training and performance! Although large batch size can improve the available computational parallelism, it can cause degradation in the performance of the model. Small batch size is found to enhance the overall generalization of the model and also uses less memory. If a smaller batch size improves the model generalization and reduces the memory footprint, it is logical to use smaller batch sizes for model training. Yann LeCun, in his tweet, suggests that a batch size of 32 is the best option for optimum model training and performance.

Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.

Artificial Intelligence (AI) is revolutionizing almost every industry. Deep Learning (DL), an AI methodology, is propelling the high-tech industry to the future with a seemingly endless list of applications ranging from object recognition for systems in autonomous vehicles to potentially saving lives -- helping doctors detect and diagnose cancer with greater accuracy. In this article, we'll outline some interesting applications of deep learning in cybersecurity and how you can use deep learning to improve security measures within your organization. Deep learning is a subtype of Machine Learning (ML) and belongs to the broader category of artificial intelligence. Deep learning uses Artificial Neural Networks (ANNs), which are designed to mimic the functionality and connectivity of neurons in the human brain. Deep learning gets its name because it uses deeper networks compared to other AI methods like ML.

Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.

An app that helps you visualize, debug, track, and run ML/DL experiments, directly on your workstation or on your own servers, in your LAN or in the cloud. Deepkit will be free for individual users and available in all app stores. You can use the app alone or use the real-time collaborative features within a team using the Deepkit team server. We're are looking for alpha users that want to help us building a better, cheaper and more efficient way of doing ML/DL experiments. If you're interested, please register at the website directly or use this link.

Intel today unveiled Nauta, a new distributed computing framework for running deep learning models in Kubernetes and Docker-based environments. The chip giant says Nauta will make it easier for data scientists to develop, train, and deploy deep learning workloads on large clusters, "without all the systems overhead and scripting needed with standard container environments." Deep learning has become one of the hottest areas of machine learning, thanks to its capability to train highly accurate predictive models in areas like computer vision and natural language processing. The technique, however, generally requires huge amounts of data and large amounts of computing horsepower, which today's enterprises typically want to manage using containers. Getting all these pieces to work together – Kubernetes, Docker, and deep learning frameworks – is complex and hard.

Intel today announced the open source release of Nauta, a platform for deep learning distributed across multiple servers using Kubernetes or Docker. The platform can operate with a number of popular machine learning frameworks such as MXNet, TensorFlow, and PyTorch, and uses processing systems that can work with a cluster of Intel's Xeon CPUs. Results of deep learning experiments conducted with Nauta can be seen using TensorBoard, command line code, or a Nauta web user interface. "Nauta is an enterprise-grade stack for teams who need to run DL workloads to train models that will be deployed in production. With Nauta, users can define and schedule containerized deep learning experiments using Kubernetes on single or multiple worker nodes, and check the status and results of those experiments to further adjust and run additional experiments, or prepare the trained model for deployment," according to a blog post announcing the news today.