Neural Networks

[00:00]: Here are five things to know about neural networks in under five minutes. Number one:

[00:06]: neural networks are composed of node layers. There is an input node layer, there is a hidden layer,

[00:16]: and there is an output layer. And these neural networks reflect the behavior of the human brain,

[00:26]: allowing computer programs to recognize patterns and solve common problems in the fields of AI and

[00:30]: deep learning. In fact, we should be describing this as an artificial neural network, or an ANN,

[00:37]: to distinguish it from the very un-artificial neural network that's operating in our heads. Now,

[00:44]: think of each node, or artificial neuron, as its own linear regression model. That's number two.

[00:51]: Linear regression is a mathematical model that's used to predict future events. The weights of the

[00:56]: connections between the nodes determines how much influence each input has on the output. So each

[01:02]: node is composed of input data, weights, a bias, or a threshold, and then an output. Now data is

[01:09]: passed from one layer in the neural network to the next in what is known as a feed forward network --

[01:17]: number three. To illustrate this, let's consider what a single node in our neural network might

[01:22]: look like to decide -- should we go surfing. The decision to go or not is our predicted outcome

[01:28]: or known as our yhat. Let's assume there are three factors influencing our decision. Are the

[01:36]: wave's good, 1 for yes or 0 for no. The waves are pumping, so x1 equals 1, 1 for yes. Is the

[01:45]: lineup empty, well unfortunately not, so that gets a 0. And then let's consider is it shark-free out

[01:52]: there, that's x3 and yes, no shark attacks have been reported. Now to each decision we assign a

[01:58]: weight based on its importance on a scale of 0 to 5. So let's say that the waves, we're going to

[02:04]: score that one, eh, so this is important, let's give it a 5. And for the crowds, that's w2.

[02:12]: Eh, not so important, we'll give that a 2. And sharks, well, we'll give that a score of a

[02:19]: 4. Now we can plug in these values into the formula to get the desired output. So yhat equals

[02:28]: (1 * 5) + (0 * 2) + (1 * 4), then minus 3, that's our threshold, and that gives us

[02:41]: a value of 6. Six is greater than 0, so the output of this node is 1 -- we're going surfing.

[02:50]: And if we adjust the weights or the threshold, we can achieve different outcomes.

[02:54]: Number four. Well, yes, but but but number four, neural networks rely on training data to learn and improve their

[03:03]: accuracy over time. We leverage supervised learning on labeled datasets to train the algorithm.

[03:08]: As we train the model, we want to evaluate its accuracy using something called a cost function.

[03:17]: Ultimately, the goal is to minimize our cost function to ensure the correctness of fit for any given

[03:23]: observation, and that happens as the model adjusts its weights and biases to fit the training data

[03:28]: set, through what's known as gradient descent, allowing the model to determine the direction

[03:33]: to take to reduce errors, or more specifically, minimize the cost function. And then finally,

[03:39]: number five: there are multiple types of neural networks beyond the feed forward neural network

[03:44]: that we've described here. For example, there are convolutional neural networks, known as CNNs, which

[03:50]: have a unique architecture that's well suited for identifying patterns like image recognition.

[03:55]: And there are recurrent neural networks, or RNNs, which are identified by their feedback loops and

[04:02]: RNNs are primarily leveraged using time series data to make predictions about future events like

[04:08]: sales forecasting. So, five things in five minutes.

[04:13]: To learn more about neural networks, check out these videos.

[04:16]: Thanks for watching.

[04:17]: If you have any questions, please drop us a line below. And

[04:21]: if you want to see more videos like this in the future, please Like and Subscribe.

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JavaScript Fundamentals Quiz

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Question 2 of 5

Question 2

Which method is used to add an element to the end of an array?

A. push()

B. add()

C. append()

D. insert()

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