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Coupon & course info

Course Name: Data Science: Deep Learning in Python

Subtitle: A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $49 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of February 19, 2016…

Students: 462 students enrolled

Ratings: 19 reviews

Rank: ranked #71 in Udemy Business Courses

Brief course description

This course will get you started in building your FIRST artificial neural network. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to K classes using the softmax function, and we derive the very important training method called “backpropagation” using first principles. I show you how to code backpropagation in Numpy, first “the slow way”, and then “the fast way” using Numpy features.

Next, we implement a neural network using Google’s new TensorFlow library.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: Intermediate Level

Lectures: 13 lessons

Duration: 1.5 Hours of video

What you get: Code a neural network from scratch in Python and numpy

Target audience: Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course

Requirements: How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons