Recipes & Perceptrons
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This document discusses using cooking as an analogy to explain neural networks. It outlines how different spices and their ratios can create different tastes, similar to how inputs and their weighted values are summed in a neural network to produce an output. The document proposes creating an online course that teaches computer programming concepts by drawing analogies between cooking recipes and programming neural networks, with the goal of helping bridge the digital divide for women in remote villages.
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Recipes & Perceptrons
- 1. RECIPES & PERCEPTRONS COOKING AS AN ANALOGY TO THE FOUNDATION OF NEURAL NETWORKS
- 2. As one adds spice to foods, different blends can made in order to give the ideal taste for the taste buds.
- 3. DIFFERENT SPICES AND THEIR BLENDS A Ratio for Spice 1 A Ratio for Spice 2 A Ratio for Spice 3 A Ratio for Spice 4 The sum of all spice portions Σ Σ is the mathematical symbol for summation.
- 4. Basically, classical neuron model can be derived from the previous slide: Input 1(𝑤1) Input 2(𝑤2 ) Input 3(𝑤3) Input 4(𝑤4) Σ Output Y w represents what are called adjusted weights
- 5. What if women who prepare meals for their families in remote villages could be introduced to analogies of cooking to the cutting edge in computer technology?
- 6. There is a digital divide between those who have digital technology skills and those who don’t. The proposed solution is an online course which would teach computer programming by means of analogies to cooking to programming of neural networks.