The game's open world is heavily influenced to the Rockstar Games' titles Midnight Club: Los Angeles and Midnight Club 3 as well as Electronic Arts' title Need For Speed: Underground 2. Petersburg, Russia, New York City, New York, Paris, France and even more settings will be added in the future via mandatory updates. The game's open world settings will be Sydney, Australia, Makati City, Philippines, Tokyo, Japan, Seoul, Korea, London, United Kingdom, Los Angeles, California, Osio, Norway, St. The game will feature powerplay elements from Split/Second: Velocity for all of the races in the game.Įlimination Races will combine the Lap Knockout race elements from the Need For Speed franchise, the fast-paced racing elements from Codemasters' Formula One games and the Powerplay elements from Split/Second: Velocity. The drifting system will be in the style of Need For Speed: Underground, Need For Speed: Underground 2, Need For Speed: Shift 2: Unleashed, every Mario Kart game to date, and even every Initial D video game.
While the transformation system is in the style of Sonic & All Stars Racing Transformed and the racing gameplay is heavily influenced to the Need For Speed games ( Need For Speed: Underground, Need For Speed: Shift, Need For Speed: Prostreet, Need For Speed: Shift 2: Unleashed and Need For Speed: Underground 2), the Burnout games and even Codemasters' Formula One games. The game will be very similar to Mario Kart but with playable characters and elements from nearly every universe like Disney, Pixar, Marvel, Lucasfilm, DC, Hasbro, Mattel, Warner Bros., Cartoon Network, DreamWorks, Universal, 20th Century Studios, Paramount, Nickelodeon, Sony Pictures, Studio Ghibli, Harvey, Square-Enix, Activision, Image Comics, Dark Horse Comics and others.
Embedding are visualized using TensorBoard Projector. To learn more about word embedding, please refer to this excellent blog post by chris colah.Īt the end of the notebook, we will find the learned embedding values relative to each other. From this, the words in future sentences could have their ‘direction’ established, and from this the sentiment inferred.
So, for example, when looking at movie reviews, those movies with positive sentiment had the dimensionality of their words ending up ‘pointing’ a particular way, and those with negative sentiment pointing in a different direction. Words are mapped to vectors in higher dimensional space, and the semantics of the words then learned when those words were labelled with similar meaning. Each word is associated with a 16-dimensional vector (or embedding) that is trained by the model. In the notebook, Keras embedding layer is used to train an embedding for each word in the vocabulary. Here, half of the dataset is used for training and the other half for testing. This dataset contains 50,000 movie reviews from IMDB, labeled by sentiment (positive/negative). For training, IMDB reviews dataset is used.
In this notebook, I learned how to classify movie reviews as positive or negative.