Supervised Learning - Classification Week 9 Challenge
1. Neural networks, also known as artificial neural networks or simulated neural networks, 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.
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2. Deep learing is the machine learning technique that teaches computer to do what comes naturally to humans.
example: In deep learning, a computer model learns to perform classification tasks from images,text or sound.
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3. An activation function is a function that is added into the an artificial neural network in order to help the network learn complex matters in data.When comparing with a neuron based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron.
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4.Types of activation function:
- Linear function
- Identity function
- Binary step function
- Sigmoid function
- Tanh function
- ReLU function (Rectified linear unit)
- Leaky ReLU function
- Softmax function
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5. Bachpropagation is (backward propagation of errors ) is an algorithm for supervised learing of artificial neural networks using gradient descent . Given an artificial neural network and an error function, the method calculates the gradient of error function with respect to the neural network's weights. The algorithm get its name because the weights are updated backwards, from output towards input.
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