Assassin's Creed is diving back in time to ancient Greece in Assassin's Creed: Odyssey and with that journey comes a handful of new features and the return of a fan favorite to keep the series fresh into its 11th installment. I had a chance to play Assassin's Creed: Odyssey at a preview event ahead of E3 and experience a few hours of the upcoming game, exploring a couple of islands in the Aegean Sea, fighting enemies, delving into tombs, and taking on the open waters. SEE ALSO: All the big trailers from Bethesda's E3 2018 showcase not called Fallout In 431 BCE, the Peloponnesian War is waging between the Spartans and the Athenians, and Greece is at your fingertips. Within the open world ahead of you, similar in style to Assassin's Creed: Origins, there is enough new stuff to set Odyssey apart from the previous games to keep things fun and interesting. Here are six features in Assassin's Creed: Odyssey that keep things spicy: Assassin's Creed: Odyssey lets you choose who to play as.

With the model size I've selected it looks like we could stop with around 15 epochs as there is no further improvement. Model's accuracy is between 0.8 and 0.85.

Spanish treasure fleets that traversed the Atlantic Ocean to the Americas and back were a 16th-century invention as important as free two-day shipping. Organized 70 years after Columbus's first voyage, the fleet was made up of several specialized ships with one primary goal: Exploiting the riches of the New World as efficiently as possible. The San José, the largest galleon and the flagship of one group of Spanish ships that started sailing in the 16th century, was big and -- thanks to 62 bronze cannons engraved with dolphins -- deadly enough to deter or destroy ships, whether pirates or rival nations. On June 8, 1708, during the War of the Spanish Succession, the San José's gunpowder ignited during a battle with British ships, sending 600 sailors to the bottom of the Atlantic Ocean -- along with gold, silver and emeralds from mines in Peru, a total haul valued at some $17 billion in today's dollars. It stands as one of the most expensive maritime losses in history.

Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation changes depending on the locality. Nonstationary dynamics pose both theoretical and practical challenges to statistical machine learning algorithms that aim to accurately capture the complexities governing the evolution of such processes. Typically the nonstationary dynamics are modeled using nonstationary Gaussian Process models (NGPS) that employ local latent dynamics parameterization to correspondingly model the nonstationary real observable dynamics. Recently, an approach based on most likely induced latent dynamics representation attracted research community's attention for a while. The approach could not be employed for large scale real world applications because learning a most likely latent dynamics representation involves maximization of marginal likelihood of the observed real dynamics that becomes intractable as the number of induced latent points grows with problem size. We have established a direct relationship between informativeness of the induced latent dynamics and the marginal likelihood of the observed real dynamics. This opens up the possibility of maximizing marginal likelihood of observed real dynamics indirectly by near optimally maximizing entropy or mutual information gain on the induced latent dynamics using greedy algorithms. Therefore, for an efficient yet accurate inference, we propose to build an induced latent dynamics representation using a novel algorithm LISAL that adaptively maximizes entropy or mutual information on the induced latent dynamics and marginal likelihood of observed real dynamics in an iterative manner. The relevance of LISAL is validated using real world datasets.

An autonomous robot will be deployed to an offshore oil and gas platform in the North Sea later this year, in a first for the sector. The £4m project's backers said the move was designed to take humans out of dangerous and dull jobs, and reinvent oil and gas as an industry of the future. Under the pilot scheme, the robot will initially be deployed at the French oil firm Total's gas plant on Shetland before being sent to join the 120 workers on the company's Alwyn platform, 440km north-east of Aberdeen. The machine, made by Austrian firm Taurob and supported on the software side by German university TU Darmstadt, will be used for visual inspections and detecting gas leaks. Rebecca Allison, asset integrity solution centre manager at the publicly-funded Oil and Gas Technology Centre, insisted autonomous robots would not be used to cut the wage burden of offshore workers who are paid a premium for working in tough, remote conditions.

Consider a number of wind turbines scattered throughout the North Sea. Each turbine is equipped with a Query processing over data streams is a key aspect of Big sensor, which continuously records temperature levels of key Data applications. For instance, algorithmic trading relies on devices within the turbine and sends those readings to a data real-time analysis of stock tickers and financial news items centre monitoring the functioning of the turbines. Temperature (Nuti et al. 2011); oil and gas companies continuously monitor levels are streamed by sensors using a ternary predicate and analyse data coming from their wellsites in order Temp, whose arguments identify the device, the temperature to detect equipment malfunction and predict maintenance level, and the time of the reading. A monitoring task in the needs (Cosad et al. 2009); network providers perform realtime data centre is to track the activation of cooling measures in analysis of network flow data to identify traffic anomalies each turbine, record temperature-induced malfunctions and and DoS attacks (Münz and Carle 2007).

The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python's scikit-learn library and then apply this knowledge to solve a classic machine learning problem.

The North Atlantic Ocean punches far above its weight when it comes to scrubbing carbon dioxide. While it accounts for less than 1.5 percent of the total surface area of the world's oceans, it captures about 20 percent of the CO2 sequestered by the seas. Cold ocean waters help trap planet-warming carbon dioxide lingering in the atmosphere. Then, algae soak up that carbon dioxide during photosynthesis, just like grasses and trees do on land.

A'crazy' artificial island in the North Sea that could supply renewable energy to 80 million people in Europe is set to open in 2027.

Every day, it seems that there is another innovation regarding how artificial intelligence can be used to do things humans couldn't do on their own. Several weeks ago, it was reported that scientists can now use AI to listen to conversations that dolphins are having with each other; an algorithm assists a research team go through millions of echolocation clicks made by these marine mammals found in the Gulf of Mexico.