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An introduction to deep learning with Brain.js - LogRocket Blog

#artificialintelligence

Using Brain.js is a fantastic way to build a neural network. It learns the patterns and relationship between the inputs and output in order to make a somewhat educated guess when dealing with related issues. One example of a neural network is Cloudinary's image recognition add-on system. I was also shocked the first time I read the documentation of Brain.js, In this post, we will discuss some aspects of understanding how neural networks work.


Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - KDnuggets

#artificialintelligence

So, using this new information let's add another node to a neural network; the bias node. Now let's do a forward propagation with the same example, x₁ 0, x₂ 0, y 0 and let's set bias, b 0 (initial bias is always set to zero, rather than a random number), and let the backpropagation of Loss figure out the bias. Well, the forward propagation with a bias of "b 0" didn't change our output at all, but let's do the backward propagation before we make our final judgment. As before let's go through backpropagation in a step by step manner. Since the derivative of bias( L/ b) is positive 0.125, we will need to adjust the bias by moving in the negative direction of the gradient(recall the curve of the Loss function from before).


Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

#artificialintelligence

So, using this new information let's add another node to a neural network; the bias node. Now let's do a forward propagation with the same example, x₁ 0, x₂ 0, y 0 and let's set bias, b 0 (initial bias is always set to zero, rather than a random number), and let the backpropagation of Loss figure out the bias. Well, the forward propagation with a bias of "b 0" didn't change our output at all, but let's do the backward propagation before we make our final judgment. As before let's go through backpropagation in a step by step manner. Since the derivative of bias( L/ b) is positive 0.125, we will need to adjust the bias by moving in the negative direction of the gradient(recall the curve of the Loss function from before).


Backward Qualitative Simulation of Structural Model for Strategy Planning

AAAI Conferences

Takenao OHKAWA, Shinya HATA, and Norihisa KOMODA Department of Information Systems Engineering Faculty of Engineering, Osaka University 2-1, Yamadaoka, Suita, Osaka 565, Japan phone: 81-6-879-7826, fax: 81-6-879-7827 email: ohkawa@ise.eng.osaka-u.ac.jp Abstract In the process of estimating the effectiveness of plans or policies, it is useful to construct a diagrammatic causal model, named the structural model, that represents causality between several factors in the target organization. We have already proposed a method for qualitative simulation that can predict behaviors of a target system modeled with a structural model for the strategy planning. The effectiveness of a supposed plan is estimated by reviewing a predicted behavior of the target as a simulation result. However, trials of the simulation have to be iterated many times in order to find out a better plan, if the model is large and complex. This paper proposes the backward simulation method that can generate possible initial states of the operable nodes from the desirable behavior of the utility nodes to cope with this problem.


NanoNeuron - 7 simple JavaScript functions that will give you a feeling of how machines can actually "learn" - Hashnode

#artificialintelligence

NanoNeuron is over-simplified version of a Neuron concept from the Neural Networks. NanoNeuron is trained to convert a temperature values from Celsius to Fahrenheit. NanoNeuron.js code example contains 7 simple JavaScript functions (model prediction, cost calculation, forward and backwards propagation, training) that will give you a feeling of how machines can actually "learn". These functions by any means are NOT a complete guide to machine learning. A lot of machine learning concepts are skipped and over-simplified there!