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Six years later, Coursera’s Andrew Ng returns with new Deep Learning courses |TTI

On August 15 2011, Stanford professor Andrew Ng uploaded an intro video to YouTube for his free online Machine Learning course. On that same day, The New York Times featured his course (along with two other Stanford courses).

The popularity of his Machine Learning course would lead him and Daphne Koller (another Stanford professor) to launch Coursera a few months later.

The intro video for Andrew Ng’s Machine Learning course.

Exactly six years later on August 15 2017, the first classes from Andrew Ng’s Deep Learning Specialization on Coursera will go live.

A lot has changed in the last six years

8 million learners have signed up for his Machine Learning course. Andrew Ng is no longer at Coursera full time, but acts as the co-chairman of the board. He left Coursera in May 2014 to join Baidu.

Back when Andrew first launched his Machine Learning course, “deep learning” wasn’t really part of our vocabulary. But in the past few years, deep learning has exploded in popularity and real world application. This might explain why Andrew Ng left Coursera to join Baidu and lead its AI lab.

The Deep Learning Specialization consists of five different courses. The courses are free to take, but you need to sign up for a subscription of $49/month if you want access to the graded assignments or earn certificates. There is a seven day free trial. The individual courses are free, but you need to visit the course pages separately (you can’t sign up to them from the Specialization page).

Though the courses officially start on 15 August, the course materials for the first three courses are already available. The individual courses are free to Audit, but you need to visit the course pages separately. You cannot sign up for these courses from the Specialization page. Follow the links below to sign up for the courses individually for free:

  1. Neural Networks and Deep Learning
  2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
  3. Structuring Machine Learning Projects
  4. Convolutional Neural Networks
  5. Sequence Models

The Specialization is targeted towards those learners who are trying to break into a career in Artificial Intelligence. Unlike his previous Machine Learning course which used Octave (an open source replacement for Matlab), Andrew’s new Specialization uses Python.

An interesting aspect of the Deep Learning courses is that learners don’t need to install anything to do the programming assignments. They are all done using Jupyter Notebooks hosted by Coursera. Coding can be done directly without leaving the browser.

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