Python Tutorial : Forward propagation

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-deep-learning-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We’ll start by showing how neural networks use data to make predictions. This is called the forward propagation algorithm. Let's revisit our example predicting how many transactions a user will make at our bank. For simplicity, we'll make predictions based on only the number of children and number of existing accounts. This graph shows a customer with two children and three accounts. The forward-propagation algorithm will pass this information through the network to make a prediction in the output layer. Lines connect the inputs to the hidden layer. Each line has a weight indicating how strongly that input affects the hidden node that the line ends at. These are the first set of weights. We have one weight from the top input into the top node of the layer, and one weight from the bottom input to the top node of the hidden layer. These weights are the parameters we train or change when we fit a neural network to data, so these weights will be a focus throughout this course. To make predictions for the top node of the hidden layer, we take the value of each node in the input layer, multiply it by the weight that ends at that node, and then sum up all the values. In this case, we get (2 times 1) plus (3 times 1), which is 5. Now do the same to fill in the value of this node on the bottom. That is (two times (minus one)) plus (three times one). That's one. Finally, repeat this process for the next layer, which is the output layer. That is (five times two) plus (one times -1). That gives an output of 9. We predicted nine transactions. That’s forward-propagation. We moved from the inputs on the left, to the hidden layer in the middle, and then from the hidden layers to the output on the right. We always use that same multiply then ad