Intro to Deep Learning

A hands-on entry point

This course treats deep learning as something you build rather than something you only read about. The emphasis is on TensorFlow and Keras applied to structured data, so the models you train are tied to tabular problems that analysts and engineers meet every day.

What you practice

• Linear units as the building blocks, and how hidden layers capture more complex relationships.
• Training with stochastic gradient descent, then diagnosing overfitting and underfitting.
• Techniques such as dropout, batch normalization and early stopping to keep models stable.

Who should take it

It suits learners who want a structured route into neural networks that stays close to practical code. The closing lesson on detecting the Higgs boson with TPUs points toward specialized hardware, a useful contrast to the everyday models that come before it.