In this guide I analyse hardware from CPU to SSD and their impact on performance for deep learning so that you can choose the hardware that you really need.

Understanding deep networks is crucial for model development and user adoption. tf-explain offers interpretability methods to gain insight on your network.

Answer (1 of 11): I'm a bit confused. Do you prototype machine learning algorithms or models? If you prototype machine learning algorithms, you shouldn't have a problem with implementing algorithms from scratch. I'll answer assuming that you meant "quickly…

Step-by-step tutorial on training object detection models on your own dataset

Posted by Yinfei Yang and Amin Ahmad, Software Engineers, Google Research Since it was introduced last year, “Universal Sentence Encoder (USE) for...

Sharing Data Science learning path

Through community-driven collaboration, we not only found the best-fit model for identifying trees but learned which approaches did not…

The mathematician Gil Kalai believes that quantum computers can't possibly work, even in principle.

Omdena is the human-centered collaborative platform to build innovative and ethical AI solutions to real-world problems.

a team of researchers from New Zealand publish their work ‘HSIC (Hilbert-Schmidt independence criterion) Bottleneck: Deep Learning without back-prop’.

A new paper by Liu, Jian, He et al introduces RAdam, or “Rectified Adam”. It’s a new variation of the classic Adam optimizer that provides…