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[D]Fathoming the Deep in Deep Learning – A Practical Approach

[D]Fathoming the Deep in Deep Learning – A Practical Approach

Deep in ‘Deep Learning’ is elusive yet approachable with a bit of mathematics. This beckons a practical question: Is elementary calculus sufficient to unravel deep learning? The answer is yes indeed. Armed with an unbound curiosity to learn and re-learn new and old alike and possibly if you can methodically follow below sections, I reckon you’ll cross the chasm to intuitively understand and apply every concepts including calculus in their glory to de-clutter all intricacies of deep learning. I’m covering the steps I took and what I researched, read and understood – being captured to reveal each concept as intuitively as possible and additional topics that piques your interest:

Read the full article @ and share your thoughts.

Steps to fathom the depth:

The Beginnings – Modelling Decisions with Perceptrons, Workhorses inside Nodes – Activation Functions, A Gentle Detour on Basics – Differential CalculusThe Underpinnings – Essential Statistics and Loss Reduction, The Grand Optimization – Gradient Descent, Intuitive Examples to the Rescue – Descent Demystified, Ensemble directed Back & Forth – Feed Forward & Back Propagation, Inner Workings of Bare NeuralNet – Matrices matched to Code & Learning Curve Retraced – References & Acknowledgements

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.