Digests » 163

this week's favorite

What everyone should know about linear algebra for machine learning

Linear algebra is the backbone of machine learning and is critical for learners to have a solid understanding of the concepts before jumping into the core of machine learning. This article covers only the essentials for a beginner looking to get started with Machine Learning.

Let’s enhance: use Intel AI to increase image resolution in this demo

Across alien epics and procedural crime dramas, detectives and truth seekers have repeated the mantra: zoom and enhance. It’s passed into popular culture as a much-beloved meme, but in recent years, machine learning has increasingly made this fiction trope into an accessible reality. And we've got the demo to prove it.

AugLy: A new data augmentation library to help build more robust AI models

We are open-sourcing AugLy, a new Python library that will help AI researchers use data augmentations to evaluate and improve the robustness of their machine learning models. Augmentations can include a wide variety of modifications to a piece of content, ranging from recropping a photo to changing the pitch of a voice recording. It’s important to build AI that isn’t fooled by these changes. AugLy helps by providing sophisticated data augmentation tools to create samples to train and test different systems.

Introducing TensorFlow Decision Forests

We are happy to open source TensorFlow Decision Forests (TF-DF). TF-DF is a collection of production-ready state-of-the-art algorithms for training, serving and interpreting decision forest models (including random forests and gradient boosted trees). You can now use these models for classification, regression and ranking tasks - with the flexibility and composability of the TensorFlow and Keras.

CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning in machine learning

The ability to reason about causality, and ask “what would happen if…?’’ is one property that sets human intelligence apart from artificial intelligence. Modern AI algorithms perform well on clearly defined pattern recognition tasks but fall short generalizing in the ways that human intelligence can.