Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Uno is the video game adaptation of the popular card game of the same name. It has been released for a number of platforms, For PlayStation 2, published by Success...
By pre-training the Perceiver on English Wikipedia and C4, the authors show that it is possible to achieve an overall score of 81.8 on GLUE after fine-tuning. Perceiver for images Now that we've seen how to apply the Perceiver to perform text classification, it is straightforward to a...
[**Walk-based approaches**](https://en.wikipedia.org/wiki/Random_walk) use the probability of visiting a node j from a node i on a random walk to define similarity metrics; these approaches combine both local and global information. [**Node2Vec**](https://snap.stanford.edu/node2vec/...
Volunteers are getting together for intense, one-day events, or events of just a few days, to build web pages, to write code, to edit Wikipedia pages, and more. These are gatherings of onsite volunteers, where everyone is in one location, together, to do an online-related project in one...
Given that I only have 5 days, I decided to [gray-box](https://en.wikipedia.org/wiki/Gray-box_testing) the first two points. You can play the result [here](https://individualkex.itch.io/ml-for-game-dev-2), and view the source code [here](https://github.com/dylanebert/FarmingG...
BERT was specifically trained on Wikipedia (~2.5B words) and Google’s BooksCorpus (~800M words). These large informational datasets contributed to BERT’s deep knowledge not only of the English language but also of our world! 🚀 Training on a dataset this large takes a long time. BERT...
BERT was specifically trained on Wikipedia (~2.5B words) and Google’s BooksCorpus (~800M words). These large informational datasets contributed to BERT’s deep knowledge not only of the English language but also of our world! 🚀 Training on a dataset this large takes a long time. BERT...
BERT was specifically trained on Wikipedia (~2.5B words) and Google’s BooksCorpus (~800M words). These large informational datasets contributed to BERT’s deep knowledge not only of the English language but also of our world! 🚀 Training on a dataset this large takes a long time. BERT...
SQuAD (Stanford Question Answering Dataset) is a reading comprehension dataset of around 108k questions that can be answered via a corresponding paragraph of Wikipedia text. BERT’s performance on this evaluation method was a big achievement beating previous state-of-the-art models and human-level...