I love machine learning, and deep learning as well. Yet, from day one of learning both, I discovered that to be better in this, I must be good at reading research papers. It takes a lot of training to learn how to read papers and summarize them. So I decided to share the world with some of those moments of decoding a research paper into summarized English text. Today I will cover Arabic Text Diacritization Using Deep Neural Networks.

Get in touch with the prominent experts within Industry 4.0 and Digital Transformation! Industry 4.0 is closely associated with more automation (compared to the Third Industrial Revolution), bridging the physical and digital worlds enabled by IIoT, Big Data, IoT, cloud computing, cognitive computing and smart factories. Investing in innovative Industry 4.0 technologies has many advantages. It boosts collaboration between departments, increases efficiency, fuels growth as well as trims costs. Real-time data and intelligence, predictive analytics and IoT machinery helps companies be proactive when it comes to solving and addressing potential supply chain management and maintenance issues. In addition, it makes it easy to optimise and manage all aspects of manufacturing processes and supply chain.

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs -- and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand. Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving, and whether there is something wrong in an image. Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data, and algorithms rather than retinas, optic nerves, and the visual cortex.

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. About the Deep Learning Specialization The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

Most of what goes by the name of Artificial Intelligence (AI) today is actually based on training and deploying Deep Learning (DL) models. Despite their impressive achievements in fields as diverse as image classification, language translation, complex games (such as Go and chess), speech recognition, and self-driving vehicles, DL models are inherently opaque and unable to explain their predictions, decisions, and actions. This is not a critical issue for several applications (such as movie recommendation systems or news/social media feed customization, for example) where the end user will evaluate the quality of the AI based on the results it produces, make occasional adjustments to help it improve future results (e.g., by rating additional movies), or move away from that product/app. There is rarely a need to require an explanation for the AI's decisions when there is very little at stake. However, for high-stakes situations and mission-critical applications – such as self-driving vehicles, criminal justice decisions, financial systems, and healthcare applications – explainability might be considered crucial.

We can dream, we can hallucinate, we can create -- so how do we build those capabilities into AI? Deep-learning expert Anima Anandkumar and distinguished theoretical physicist John Ellis discuss the potential for artificial intelligence to one day collaborate with us in attacking the biggest unanswered questions in physics -- questions which have outwitted humans for years. In a conversation ranging from the quantum nature of subatomic reality to the distributed intelligence of the octopus, our guests explore how AI might one day tackle questions which are conceptually boundless and infinite. "This would be truly stealing the theoretical physicists' lunch," says Ellis.

Artificial Intelligence (AI) has been redefining society in ways we have never anticipated. Technology is clinging to us in every walk of our lives, right from unlocking our smartphones to our day-to-day activities, online shopping, intelligent car dashboards, autonomous robots and so on. Though the concept of AI was first talked about in the early 1950s, forming a basis for many computer learning and complex decision-making processes, it is only of late, where processing huge amounts of data is required, that this field of technology is picking up pace. What is in the AI basket? AI is not a technology, rather it is a science or field of study.

In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. This project is for anyone with foundation in programming and machine learning who wants to develop Data science and Machine learning projects but having limited resources on their computer and limited time. You will learn how to use the Google Colaboratory via your web browser to develop a Fake and Real News Detection Data Science Project.

Copilot launched last week in an invite-only Technical Preview, promising to save time by responding to users' code with its own smart suggestions. Those suggestions are based on billions of lines of public code that users have publicly contributed to GitHub, using an AI system called Codex from the research company OpenAI. GitHub describes Copilot as the AI equivalent of pair programming, in which two developers work together at a single computer. The idea is that one developer can bring new ideas or spot problems that the other developer might've missed, even if it requires more person-hours to do so.

What's the best way to arrange wells in an oil or gas field? It's a simple enough question, but the answer can be very complex. Now a Cal Tech/JPL spinoff is developing a new approach that blends traditional HPC simulation with deep reinforcement learning running on GPUs to optimize energy extraction. The well placement game is a familiar one to oil and gas companies. For years, they have been using simulators running atop HPC systems to model underground reservoirs.