Build a Neural Net in 4 Minutes

Key Takeaways

oh hello world welcome to audiology today we're going to be building a neural net in four minutes let's get started they're like a million-in-one machine learning models out there but neural nets in particular have gotten really popular recently because of two things that's our computers and more data they've helped produce some amazing breakthroughs in everything from image recognition to generating rap songs they're really just three steps involved in machine learning build it train it and test it once we build our model we can train it against our input and output data to make it better and better at pattern recognition so let's build our model a three layer neural network in Python we'll want to start off by importing numpy which is my gotu library for scientific computing in python then we'll want to create a function that will map any value to a value between zero and one this is called a sigmoid this function will be run in every neuron of our network when data hits it it's useful for creating probabilities out of numbers once we've created that let's initialize our input data set as a matrix each row is a different training example each column represents a different neuron so we have four training examples with three input neurons each then we'll create our output data set for examples one output neuron each since we'll be generating random numbers in a second let's seed them to make them deterministic this just means give random numbers that are generated the same starting point or seed so that we'll get the same sequence of generated numbers every time we run our program this is useful for debugging next we'll create our synapse matrices synapses are the connections between each neuron in one layer to every neuron in the next layer since we'll have three layers in our network we need to synapse matrices each synapse has a random weight assigned to it after that we'll begin the training code we'll create a for loop that iterates over the training code to optimize a network for the given data set we'll start off by creating our first layer it's just our input data now comes the prediction step we'll perform matrix multiplication between each layer and it's synapse then we'll run our sigmoid function on all the values in the matrix to create the next layer the next layer contains a prediction of the output data then we do the same thing on that layer to get our next layer which is a more refined prediction so now that we have a prediction of the output value and layer two let's compare it to the expected data using subtraction to get the error rate will also want to print out the average error rate at a set interval to make sure it goes down every time next we'll multiply our error rate by the result of our sigmoid function the function is used to get the derivative of our output prediction from layer two this will give us a delta which we'll use to reduce the error rate of our predictions when we update our synapses every iteration then we'll want to see how much layer one contributed to the error in layer two this is called back propagation we'll get this error by multiplying layer twos Delta by synapse Ones transpose then we'll get layer ones Delta by multiplying its error by the result of our sigmoid function the function is used to get the derivative of layer one now that we have Delta's for each of our layers we can use them to update our synapse weights to reduce the error rate more and more every iteration this is an algorithm called gradient descent to do this we'll just multiply each layer by a delta finally let's print the predicted output and there you have it let's run this in terminal and see what we get awesome we can see that our error rate decreases every iteration and the predicted output is very very close to the actual output there is so much we can do to improve our neural network for more information check out the links in the description below and please subscribe for more technology videos thanks for watching