Explore >> Select a destination
|
You are here 
|
practicalcheminformatics.blogspot.com | ||
| | | | |
deepmind.google
|
|
| | | | | This has been a year of incredible progress in the field of Artificial Intelligence (AI) research and its practical applications. | |
| | | | |
www.interviewbit.com
|
|
| | | | | Table Of Contents show Machine Learning Methods Who's Using ML and What is it Used for? Best Machine Learning Books 1. Hands-on ML with Scikit-Learn, Keras & | |
| | | | |
telesens.co
|
|
| | | | | [LatexPage] This post shows how to set up a local Ray cluster consisting of several Ubuntu workstations connected over a home WiFi network and running a Directed Acyclic Graph (DAG) of computations on this cluster. The DAG comprises loading a CSV file from AWS S3, performing simple data transformations steps, training a RandomForest classifier, running various | |
| | | | |
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... | ||
