Data Science Lecture 4: Introduction to Deep Learning

Deep learning can ingest and process ______ data, like text and images

In machine learning, this hierarchy of features is established manually by a ______ expert

Deep Learning learns features directly from the ______

Deep learning model would require more data points to improve its ______

Since deep-learning algorithms require a tonne of data to learn from, this increase in data creation is one ______

Deep learning is a neural network with ______ or more layers.

Deep learning attempts to simulate the behavior of the ______.

Deep learning is a sub-field of ______.

Deep learning distinguishes itself from classical machine learning by the type of data it works with and the methods in which it ______.

Machine learning algorithms leverage structured, labeled data to make ______.

What distinguishes deep learning from classical machine learning?

How does deep learning attempt to simulate the behavior of the human brain?

What does machine learning leverage to make predictions?

Why is deep learning popular compared to other machine learning techniques?

How do deep learning models differ from traditional machine learning models?

What is a key difference between deep learning and machine learning?

How does deep learning handle feature extraction compared to machine learning?

In what way does deep learning differ from machine learning in handling high dimensional data?

What makes deep learning popular according to the text?

Why is deep learning able to ingest and process unstructured data like text and images?