How You Should Read Research Papers According To Andrew Ng

https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3

How You Should Read Research Papers According To Andrew Ng (Stanford Deep Learning Lectures)
“Wisdom is not a product of schooling but of the lifelong attempt to acquire it.” — Albert Einstein. Introduction. The ability to understand information produced by the individuals at the cutting edge of research within Artificial Intelligence and the Machine learning domain is a skill that every serious machine learning practitioner should acquire.
towardsdatascience.com

Are Better Machine Training Approaches Ahead?

https://semiengineering.com/are-better-machine-training-approaches-ahead

Are Better Machine Training Approaches Ahead?
We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic.But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viable.
semiengineering.com

Deep Learning Models for Automatic Summarization – Towards Data Science

https://towardsdatascience.com/deep-learning-models-for-automatic-summarization-4c2b89f2a9ea

Deep Learning Models for Automatic Summarization – Towards Data Science
Figure 1: Basic Seq2Seq encoder-decoder architecture with attention.The x_i are the input token embeddings, the a_i^t are the attention weights at step t, the h_i are the context vectors, h^t is the sentence embedding at step t obtained by weighting the context vectors with the attention weights, s_i are the decoder states, x’_i are the embeddings of the generated token (at inference time …
towardsdatascience.com