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.

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)