Just to recap from our last post on Getting Started with PyTorch Lightning, in this tutorial we will be diving deeper into two additional tools you should be using: TorchMetrics and Lightning Flash. TorchMetrics unsurprisingly provides a modular approach to define and track useful metrics across batches and devices, while Lightning Flash offers a suite of functionality facilitating more efficient transfer learning and data handling, and a recipe book of state-of-the-art approaches to typical deep learning problems. We'll start by adding a few useful classification metrics to the MNIST example we started with earlier. We'll also swap out the PyTorch Lightning Trainer object with a Flash Trainer object, which will make it easier to perform transfer learning on a new classification problem. First things first, and that's ensuring that we have all needed packages installed.

Deep Learning is a technique that is a subset of machine learning (ML), that refers to training of Neural Networks. Machine Learning allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Deep Learning is preferred in cases where the data is unstructured and massive. It is inspired by the structure of the human brain-- thereby the analogy, "Neural Networks" . It is the most efficient way to deal with unstructured data, as it extracts patterns from the raw data using neural networks. Neural Networks were a field discovered around the early 1950s.

Artificial intelligence and machine learning (AI and ML) are key technologies that help organizations develop new ways to increase sales, reduce costs, streamline business processes, and understand their customers better. AWS helps customers accelerate their AI/ML adoption by delivering powerful compute, high-speed networking, and scalable high-performance storage options on demand for any machine learning project. This lowers the barrier to entry for organizations looking to adopt the cloud to scale their ML applications. Developers and data scientists are pushing the boundaries of technology and increasingly adopting deep learning, which is a type of machine learning based on neural network algorithms. These deep learning models are larger and more sophisticated resulting in rising costs to run underlying infrastructure to train and deploy these models.

Researchers have successfully developed an AI system capable of completing mathematics problems at a grade school level, a new report asserts. Traditionally, while AI models are proficient at manipulating language to formulate sentences, the multi-step reasoning required to solve math problems has been a step too far. However, researchers at OpenAI (the company behind language model GPT-3) say they have trained a model to recognize its own mistakes, which means it can repeatedly reassess until it discovers a workable solution. In testing, the AI system was able to solve almost as many problems as a sample of children between the ages of nine and twelve. The children scored 60% on a test drawn down from the OpenAI database, while the AI system scored 55%.

Given a set of RGB images and an initial 3D reconstruction (left), this inverse rendering approach is able to synthesize novel frames and optimize the scene's parameters (right). Reproduced under a CC BY 4.0 license. Here are the most tweeted papers that were uploaded onto arXiv during October 2021. Results are powered by Arxiv Sanity Preserver. Abstract: We present a novel point-based, differentiable neural rendering pipeline for scene refinement and novel view synthesis.

AMD and Nanyang Technological University, Singapore (NTU Singapore), with the support of the Singapore Economic Development Board (EDB) have announced the official opening of the NTU-AMD Data Science and Artificial Intelligence (DS&AI) Lab. With a dedicated goal of nurturing the next generation of globally-competitive tech leaders and innovators, AMD and NTU's synergised efforts will augment local DS&AI education, equipping students with the tech tools and skills needed to inspire a better future. In line with Singapore's efforts to build a thriving AI ecosystem, the NTU-AMD DS&AI Lab will combine AMD's leading-edge deep-learning technology and NTU's global strengths in machine learning, AI and data science, enabling local DS&AI talent to pioneer the development of impactful AI solutions in key sectors. This initiative also aims to support the AI Singapore programme and the nation's transition to Industry 4.0

When dealing with supervised learning within deep learning, we might say that there are some classical approaches to follow. The first solution is the so-called "heroic" strategy where one creates a completely new deep neural network (DNN) from scratch and train/evaluate it. In practical terms, this solution may not be very interesting since there are countless DNNs available nowadays, like many deep convolutional neural networks (CNNs), that can be reused. The second path is simply to consider a deployable DNN, trained for a certain context, and see its operation in another context. Despite all the advances in deep learning, models can present bad performances if the contexts are too diverse.

OpenAI, the "capped" for-profit company founded by Elon Musk, is continuing to improve its GPT-3 language model and is developing a tool for summarising books or texts. It's a development that could benefit businesses in particular. It has now become possible to create summaries for entire books with artificial intelligence. While the field already counts some specialised companies such as Primer plus some trial work in the area from the likes of Facebook and Google, OpenAI is now positioning itself in the sector. The association has developed an AI model capable of summarising books, or more simply texts, documentation and even studies. In fact, the model summarizes small sections of a text and then adds them together before creating a more refined and precise summary.

In today's society, we see many cars obtain features that allow the vehicle to do more work than the driver, such as keeping the driver in the correct lane, auto-brake, self-parking, and we've recently seen 100% self-driving cars hit the road. But, the truth is, many of us are still wondering -- what kind of technologies allow the vehicle to know whether it's a red or green light -- or where the lines on the road are. And behind all of this is Convolutional Neural Networks. Convolutional Neural Networks, also known as CNNs, are a form of neural networks that are popular for image analysis, computer vision, data analysis or classification problems. CNNs are known to pick out certain patterns and sense them through using convolutional layers.

Crowdsourcing systems enable us to collect noisy labels from crowd workers. A graphical model representing local dependencies between workers and tasks provides a principled way of reasoning over the true labels from the noisy answers. However, one needs a predictive model working on unseen data directly from crowdsourced datasets instead of the true labels in many cases. To infer true labels and learn a predictive model simultaneously, we propose a new data-generating process, where a neural network generates the true labels from task features. We devise an EM framework alternating variational inference and deep learning to infer the true labels and to update the neural network, respectively. Experimental results with synthetic and real datasets show a belief-propagation-based EM algorithm is robust to i) corruption in task features, ii) multi-modal or mismatched worker prior, and iii) few spammers submitting noises to many tasks.