Convolutional Neural Networks: Zero to Full Real-World Apps Udemy Free Download

What you'll learn:

Requirements::

• 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)

Description:

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).

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***Read the Quick FAQ for the entire course lowdown!***

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• 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)

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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.

Who this course is for:

• Professionals and/or Enthusiasts that need to create a CNN Real-World Application

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