Online Courses Udemy | The Complete Self-Driving Car Course - Applied Deep Learning, Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python | Created by Rayan Slim, Amer Sharaf, Jad Slim, Sarmad Tanveer Preview this course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Neural Network with Attention Mechanism for Natural Language Processing: survey

Within the last decade, some of the most notable breakthroughs in artificial intelligence (AI) have come in the form of computer vision. Essentially giving robotics systems'eyesight', in the ability to identify and classify objects using image or video recognition, the technology has been put to use in anything from facial recognition systems and quality control in manufacturing to anomalies in MRI scans and self-driving vehicle systems. And while computer vision applications are still comparatively nascent, the'breakthrough' AI applications of the decade ahead might well come in the form of advances in language-based applications. AI research and deployment company OpenAI developed the largest language model ever created this year, GPT-3. The software can generate human-like text on demand and is set to be turned into a commercial product later this year, as a paid-for subscription via the cloud for businesses.

Are you an AI enthusiast who wants to keep abreast of the latest developments in space but do not know where to begin? This Mumbai-based computer science engineer may have an answer for you. With the number of papers and publications that are published each week growing exponentially, one of the biggest challenges for the AI and machine learning enthusiasts is to pick the papers that are trending in the space. There are very few dedicated platforms that host the archives of the technical papers and even fewer websites that surface and suggest top trending papers in AI, ML, computer vision and related domains. In fact, 42papers is one of a kind initiative that lets tech enthusiasts pick from the top trending papers.

Recurrent Neural Networks (RNNs) are neural networks that recall each and every information through time. In the past few years, this neural network has gained much traction and has been utilised in several applications. The applications include speech recognition, machine translation, video tagging, text summarization, prediction and more. Here, we have listed the top 10 open-source projects on Recurrent Neural Networks (RNNs), in no particular order, that one must try their hands on. About: This project is about Human Activity Recognition (HAR) using TensorFlow on smartphone sensors dataset and an LSTM RNN.

This might be the first time you hear about Explainable Artificial Intelligence, but it is certainly something you should have an opinion about. Explainable AI (XAI) refers to the techniques and methods to build AI applications that humans can understand "why" they make particular decisions. In other words, if we can get explanations from an AI system about its inner logic, this system is considered as an XAI system. Explainability is a new property that started to gain popularity in the AI community, and we will talk about why that happened in recent years. Let's dive into the technical roots of the problem, first.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Learning from diverse offline datasets is a promising path towards learning general purpose robotic agents. However, a core challenge in this paradigm lies in collecting large amounts of meaningful data, while not depending on a human in the loop for data collection. One way to address this challenge is through task-agnostic exploration, where an agent attempts to explore without a task-specific reward function, and collect data that can be useful for any downstream task. While these approaches have shown some promise in simple domains, they often struggle to explore the relevant regions of the state space in more challenging settings, such as vision based robotic manipulation. This challenge stems from an objective that encourages exploring everything in a potentially vast state space. To mitigate this challenge, we propose to focus exploration on the important parts of the state space using weak human supervision. Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human provided images of important states. These human provided images only need to be collected once at the beginning of data collection and can be collected in a matter of minutes, allowing us to scalably collect diverse datasets, which can then be combined with any batch RL algorithm. We find that BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot, and observe that compared to task-agnostic and weakly-supervised exploration techniques, it (1) interacts more than twice as often with relevant objects, and (2) improves downstream task performance when used in conjunction with offline RL.

Inference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO). Many sampling-based methods have been proposed for estimating these gradients, but they suffer from high bias or variance. In this paper, we propose a new approach that leverages the tractability of probabilistic circuit models, such as Sum Product Networks (SPN), to compute ELBO gradients exactly (without sampling) for a certain class of densities. In particular, we show that selective-SPNs are suitable as an expressive variational distribution, and prove that when the log-density of the target model is a polynomial the corresponding ELBO can be computed analytically. To scale to graphical models with thousands of variables, we develop an efficient and effective construction of selective-SPNs with size $O(kn)$, where $n$ is the number of variables and $k$ is an adjustable hyperparameter. We demonstrate our approach on three types of graphical models -- Ising models, Latent Dirichlet Allocation, and factor graphs from the UAI Inference Competition. Selective-SPNs give a better lower bound than mean-field and structured mean-field, and is competitive with approximations that do not provide a lower bound, such as Loopy Belief Propagation and Tree-Reweighted Belief Propagation. Our results show that probabilistic circuits are promising tools for variational inference in discrete graphical models as they combine tractability and expressivity.