What you'll learn :
Understand the Similarities and Differences between NN vs. CNN
Understand CNN Concepts and Architectures
Analyze Live and Interactive CNN Applications
Create your first CNN Real-World Application
Submit your CNN Final Assignment for Final Review
Friendly plain-English and direct to point explanations
1. Python +3.0
2. Keras +2.0
3. Your own Images Set (for Final Assignment project)
4. My NN course (Optional, but highly recommended)
Some Student Reviews:
“5/5 stars to Mauricio!” (March 2018).
“The implementation part is very good and up-too the mark. The explanation step by step process is very good.” (February 2018).
“course done very well; everything is explained in detail; really satisfied !!!” (February 2018).
“Difficult topics are simply illustrated and therefore easy to understand.” (January 2018).
“So far the course is good, clearly Explanation.” (November 2017).
***Read the Quick FAQ for the entire course lowdown!***
NEW: Final Assignment submission lecture! Send in your CNN app and I’ll review it!
NEW: Trophy Awards for Key Section Achievements!
BONUS: Artificial Neural Networks Summary (for your Review and refreshment)
As always, thanks for showing interest in this course!
What makes this course special:
Convolutional Neural Networks (CNN): Concepts, Visual Examples and Presentations
Step-by-Step CNN Creation and Training
Create your CNN application using your own Images
Plus, personalized feedback and help.
You ask, I answer directly!
You’ll start with the Neural Networks Review:
Quickly learn/refresh all about Neural Networks (NNs): Feed-Forward Passes, Gradient Descent and Backpropagation,
Refresh your memory about how NNs learn from data,
After this, you will be ready and set to tackle Convolutional Neural Networks.
You’ll start your Convolutional Neural Networks endeavor by reviewing their history and motivation:
Why are they so good at prediction?
What makes them so special?
What were the first attempts?
You’ll continue your Convolutional Neural Networks endeavor by going into all required concepts:
How does Convolutional Neural Networks read images?
What’s a Convolution layer and how to interpret it?
What are the main components of Convolutional Layers?
Then, learn how all Neural Network concepts stack into Convolutional Layers, i.e. activations, losses,
Before jumping into code, you’ll see some Convolutional Neural Networks action:
You’ll see 2 Convolutional Neural Networks LIVE,
See how they learn right in front of your eyes,
You’ll do exactly the same thing in the next sections! So go for it!
You’ll code your first Convolutional Neural Networks application:
Code using the famous MNIST dataset,
Easily understand all learnt concepts applied in this section,
Tweak parameters according to your criteria and get a feel about how Convolutional Neural Networks learn from images.
Now it’s time for you to code your own Convolutional Neural Networks app with your own images:
We’ll use the hydrangea (Kaggle) image dataset competition,
Learn how to “take” images from your PC for your Convolutional Neural Networks app,
Modify the parameters for the best learning process.
Submit your own Convolutional Neural Networks app as the course’s Final Assignment:
Get comments on how to make it better
Learn 100% by applying all concepts in this assignment
Optimize for best results
Lastly, you can post questions or doubts, and I’ll answer to you personally.
I’ll see you inside,
-M.A. Mauricio M.
Who this course is for :
Professionals and/or Enthusiasts that need to create a CNN Real-World Application
Course Size Details :
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