The axon is the lengthy part of the neuron which simply propagates a “spike” of energy to the axon terminals, which originates from the dendrites. Some of these dendrites might be easier to stimulate than other based on various factors The dendrites are small tree-like structures that are stimulated when enough neurotransmitters are dumped into the synapse between a neuron and the end of the dendrite. The main parts that we are going to focus on: To explain briefly, neural networks are modeled after the same neurons that exist in our brain.įor our purpose we are going to focus on only a few of the main features of a neuron which are critical to the model of neuron. In other words, we are going to make this neural network learn by giving it a set of “correct” input and output values, which it will use to calibrate, or change its own structure to make sure that it also outputs the correct values. For the purpose of this post, I’m going to assume that we’re teaching this neural net via supervised learning. I found it so fantastic actually that I wanted to share the basic concepts here. I had watched an MIT OpenCourseWare Lecture which gave a rough introduction to neural networks which I found to be quite captivating. They are useful because they can help uncover hidden patterns in a plethora of data, or be taught to perform certain tasks such as handwriting or facial recognition. Specifically I had been researching neural networks. The idea of teaching a computer how to “learn” just seems intriguing to me. I’m new to some of the topics in this field but I have been introduced to them before. The other day I had been looking up information on machine learning. This is the reason for the term stochastic in SGD, as it provides a noisy estimate for the gradient of the entire dataset.Neural Networks and the Backpropagation Algorithm Zac Blanco Blog Education Projects About Neural Networks and the Backpropagation Algorithm What separates SGD from GD is that, in each update step, only one sample is fed into the cost function rather than the entire dataset. However, if we assume a large number of parameters and data samples, that is not computationally feasible. We could use GD to compute the gradient of the cost function. For example, a sensible choice for regression problems is the Mean Square Error: The type of model prediction will inform which cost function is most appropriate. We’ll use a cost function, which we’ll aim to minimize. įinally, we need to decide how to quantify the difference between the desired and model outputs. Though it is not typically the case, let’s assume this is done for the entire dataset. To do that, we need to decide on the desired output. Let’s use these outputs to tune the parameters of the model. The dataset is fed into this model to produce outputs.
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