What is the function of a neural network?
A neural network is an attempt to replicate human brain and its network of neurons. An ANN artificial neural network is made up of artificial neurons or nodes. An ANN is basically applied for solving artificial intelligence (AI) problems.
How do you ensure performance of a neural network?
There are some techniques to avoid overfitting:
- Regularisation of data (L1 or L2).
- Dropouts — Randomly dropping connections between neurons, forcing the network to find new paths and generalise.
- Early Stopping — Precipitates the training of the neural network, leading to reduction in error in the test set.
Can neural networks approximate any function?
The Universal Approximation Theorem states that a neural network with 1 hidden layer can approximate any continuous function for inputs within a specific range. If the function jumps around or has large gaps, we won’t be able to approximate it.
How are neural networks used in data science?
Neural networks are machine learning algorithms that provide state of the accuracy on many use cases. But, a lot of times the accuracy of the network we are building might not be satisfactory or might not take us to the top positions on the leaderboard in data science competitions.
Which is the simplest function in a neural network?
The activation function takes into account the interaction effects in different parameters and does a transformation after which it gets to decide which neuron passes forward the value into the next layer. We start off with the simplest function; the linear function.
How is Relu improving the performance of neural networks?
But, they suffered from the problem of vanishing gradients, i.e during backpropagation, the gradients diminish in value when they reach the beginning layers. This stopped the neural network from scaling to bigger sizes with more layers. ReLU was able to overcome this problem and hence allowed neural networks to be of large sizes.
Why do you have to train a neural network?
Therefore, you have to train the network for a longer period of time. A small learning rate also makes the network susceptible to getting stuck in local minimum. i.e the network will converge onto a local minima and unable to come out of it due to the small learning rate. Therefore, you must be careful while setting the learning rate.