/explore
|
You are here 
|
blog.acolyer.org | ||
| | | | |
mccormickml.com
[AI summary] The tutorial provides a comprehensive guide to extracting and analyzing BERT embeddings. It begins with tokenization and segment embedding creation, followed by the calculation of word and sentence embeddings using different strategies such as summation and averaging of hidden layers. The context-dependent nature of BERT embeddings is demonstrated by comparing vectors for the word 'bank' in different contexts. The tutorial also discusses pooling strategies, layer choices, and the importance of context in generating meaningful embeddings. It concludes with considerations for special tokens, out-of-vocabulary words, similarity metrics, and implementation options. |
|
| | | | | ||
| | | | |
www.mlpowered.com
Blog posts and other information |
|
| | | | | ||
| | | | |
towardsml.wordpress.com
How is Google Translate able to understand text and convert it from one language to another? How do you make a computer understand that in the context of IT Apple is a company and not a fruit? Or how is a Smart-Keypad able to predict the most likely next few words that you are going... |
|
| | | | | ||
| | | | |
jaketae.github.io
Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... |
|
| | | |||
