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Introduction to Neural Networks (NNs) and Deep Learning (DL)

This blog post provides a foundational overview of Neural Networks and Deep Learning concepts.

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Yordan Arango

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Activation functions in Deep Learning (DL)

An explanation on why activation functions are fundamental to DL and what types can we use.

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Yordan Arango

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Gradient descent: a brute force (but a smart one) optimization technique

An explanation on how to reach the optimum values for weights and bias in NNs.

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Yordan Arango

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Entropy, or why everything can go wrong

An exploration of the several analogies in which the of entropy can be applied to

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Yordan Arango

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Entropía, o de porqué todo puede "malir sal"

Una exploración de las muchas analogías en las que la entropía es aplicada

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Yordan Arango

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Entropía, o de porqué todo puede "malir sal" (II)

Una exploración de las muchas analogías en las que la entropía es aplicada

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Yordan Arango

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Backpropagation: a numeric method to compute the error gradient in Deep Learning

How to implemenet the Gradient Descent technique when the error is not an analytical equation

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Yordan Arango

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Math and statistical tools for Deep Learning

All the math and statistical concepts needed to develop the theory behind Deep Learning and Neural Networks

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Yordan Arango