A Gentle Introduction to Deep Learning Using Keras
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A Gentle Introduction to Deep Learning Using Keras

A Visual Learners Guide to Building Neural Networks Using Keras
4.1 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
51 students enrolled
Created by Mike West
Last updated 4/2017
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  • 1 hour on-demand video
  • 4 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • At the end of the course you'll understand how to create an end to end deep learning model using the Keras Library in Python.
  • In the course we will walk through every line of code so you'll be able to understand the model and the process.
  • You will also receive a completed Jupyter Notebook filled with models and references.
  • You'll also learn how to adjust key parameters of the model in order to get better performance out of it.
View Curriculum
  • You'll need to understand the basics of Python.

Welcome to A Gentle Introduction to Deep Learning Using Keras.

Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.

It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.

In this course, we are going to build an end-to-end Python machine learning project.  You’ll learn how to use Keras to build and tune a deep neural network.

Keras is quickly becoming the de facto tool to do deep learning in Python, especially for beginners. Its minimalist, modular approach makes it simple to get deep neural networks up and running.

A Jupyter notebook is a web app that allows you to write and annotate Python code interactively. It's a great way to experiment, do research, and share what you are working on.

In this course all of the tutorials will be created using jupyter notebooks. In the preview lessons we install Python. Check them out. They are completely free.

We will also gently introduce you to the vernacular of deep learning. For example, a deep neural network is simply a neural network with more than one hidden layer. That’s it. 

Actually, a hidden layer really means "not an input or an output." 

Why all the hype around deep learning? While much of the hype in the IT world is just that, the hype around deep learning may be the real thing. Recently, deep learning models have been outperforming every other kind of machine learning model. 

You’ll get hands on experience with the process of machine learning. The process involves importing data, cleaning the data, training and testing, pre-processing and feature engineering.

We are going to define new terms but we will skip the math and theory for now.

Thanks for your interest in A Gentle Introduction to Deep Learning Using Keras.

See you in the course!!!!

Who is the target audience?
  • If you're interested in Machine Learning and Deep Learning then this course is for you.
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Curriculum For This Course
Course Introduction
10 Lectures 28:17

This is an introduction to the course. 

What is Keras and what are we going to learn?

Preview 02:17

We need micro goals in order measure our learning. 

What are we going to learn in the course. 

Preview 02:01

While this isn't a comprehensive definition it will give you a solid starting point in defining what deep learning really is. 

Preview 07:34

The Perceptron

What is prediction analysis. 

Let's define in it relation to our every day life. 

Predictive Modeling

This is our IDE. 

It's super easy to learn. 

Let's get it installed here. 

Jupyter Notebook Anatomy

Installing Python

This lecture will show you where to put the Jupyter Notebook and why I included 2 .csvs for the course. 

Course Downloads How To

This will have the downloads for the course. 

Course Download


5 questions
Building a Keras Neural Network
9 Lectures 29:20

We need to import our libraries in order to use their functionality. 

Importing Our Libraries and Modules

Let's load out data set in this lecture. 

Loading Our Data

Let's create an array in order to split out our variables. 

Splitting Out X and Y

In this lecture let's build the core Keras model. 

Defining the Keras Model

In this brief lecture let's compile the model. 

Compiling the Model

Let's fit our model to our data. 

Fitting our Model

Let's walk through the process running the entire model. 

The End to End Process

Let's attempt to squeeze some more performance out of our model. 

Tuning the Model


10 questions
About the Instructor
Mike West
4.1 Average rating
2,625 Reviews
43,339 Students
40 Courses
SQL Server and Machine Learning Evangelist

I've been a production SQL Server DBA most of my career.

I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman.

Experience, education and passion

I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car.


Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT).


Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.