Neural Network.pptx
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Neural networks are a type of machine learning that are inspired by the human brain. They are composed of interconnected nodes organized into layers that process input data and pass outputs between layers. Each node receives weighted input, processes it using an activation function, and if the output exceeds a threshold the node activates and passes data to the next layer. Through training on labeled datasets, neural networks can learn patterns in the data and perform tasks like image recognition or classification.
Neural Network.pptx
- 1. Neural Network By. Mohamed Essam
- 2. Simple Neural Network Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), ANN are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
- 3. Simple Neural Network Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
- 5. How do neural networks work? Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:
- 6. How do neural networks work? Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y) is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
- 7. How do neural networks work? All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network.
- 8. Simple Neural Network First node in hidden layer = W1 * X1 + W2 * X2 1 * 2 + 1 * 3 = 5 Second node in hidden layer = W3 * X1 + W4 * X2 -1 * 2 + 1 * 3 = 1 Output node = W5 * X1 + W6 * X2 2 * 5 + -1 * 1 = 9
- 9. Simple Neural Network An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations.
- 10. Simple Neural Network An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations.
- 11. Simple Neural Network An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations.
- 13. Let’s assume that there are three factors influencing your decision-making: 1.Are the waves good? (Yes: 1, No: 0) 2.Is the line-up empty? (Yes: 1, No: 0) 3.Has there been a recent shark attack? (Yes: 0, No: 1) Then, let’s assume the following, giving us the following inputs: •X1 = 1, since the waves are pumping •X2 = 0, since the crowds are out •X3 = 1, since there hasn’t been a recent shark attack
- 14. Now, we need to assign some weights to determine importance. Larger weights signify that particular variables are of greater importance to the decision or outcome: •W1 = 5, since large swells don’t come around often •W2 = 2, since you’re used to the crowds •W3 = 4, since you have a fear of sharks Finally, we’ll also assume a threshold value of 3, which would translate to a bias value of –3. With all the various inputs , we can start to plug in values into the formula to get the desired output.
- 15. As we start to think about more practical use cases for neural networks, like image recognition or classification, we’ll leverage supervised learning, or labeled datasets, to train the algorithm. As we train the model, we’ll want to evaluate its accuracy using a cost (or loss) function. This is also commonly referred to as the mean squared error (MSE). In the equation below,
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