Deep neural networks are a powerful method for automatically learning distributed representations at multiple levels of abstraction. Over the past decade, they have dramatically pushed forward the state-of-the-art in domains as diverse as vision, language…

Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them -- and the rich structure of this combinatorial space.

We are living in the age of big data today. New datasets are released every now and then. Moreover, datasets’ size is growing too. While…

There’s a flood of ML models/implementations out there. Here’s 4 steps to picking the right one for your next project!

This is the third part in a series on Convnets. Read the first part here and the second part here.

One of the main questions throughout human history and the study of the brain is this: how do we perceive the world the way we do?

Learn about Python text classification with Keras. Work your way from a bag-of-words model with logistic regression to more advanced methods leading to convolutional neural networks. See why word embeddings are useful and how you can use pretrained word embeddings.…

Contains Jupyter Notebooks/Resources provided by the author and my work on problem sets. - WillKoehrsen/Hands-On-Machine-Learning

Object detection is a branch of computer vision, in which visually observable objects that are in images of videos can be detected, localized, and recognized by computers. An image is a single frame that captures a single-static instance of a… Continue reading…

Part five of the 50 datasets series by Cambridge Spark, Data Science experts