or subscribe with
Join 0+ readers for one email each week.
Digests » 24
this week's favorite
Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained.
Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years.
A github-based course covering a range of topics from embeddings to sequence-to-sequence learning with attention.
Describing an image is easy for humans, and we are able to do it from a very young age. In machine learning, this task is a discriminativeclassification/regression problem, i.e. predicting feature labels from input images.
As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relevant to the field. For example - a 15 minute tutorial on Tensorflow using MNIST dataset, or a 10 minute intro to Deep Learning in Keras on Imagenet.