Convolutional Neural Networks: Zero to Full Real-World Apps

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)
═════════════════════════════════════════════════════
Hi. 
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!

✅ First:

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.

✅ Second: 
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?

✅ Third:
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,

✅ Forth:
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!

✅ Fifth:
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.

✅ Sixth:
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.

✅ Seventh:

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.