Explore >> Select a destination
|
You are here 
|
www.wjst.de | ||
| | | | |
publications.copernicus.org
|
|
| | | | | ||
| | | | |
blog.scienceopen.com
|
|
| | | | | One main aspect of open peer review is that referee reports are made publicly available after the peer review process. The theory underlying this is that peer review becomes a supportive and collaborative process, viewed more as an ongoing dialogue between groups of scientists to progressively asses the quality of research. Furthermore, it opens up [...] | |
| | | | |
www.eg-quaternary-science-journal.net
|
|
| | | | | [AI summary] The text describes search functionality issues and a redirect to an open-access journal website. | |
| | | | |
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... | ||
