Table of Contents
- 1 What is redundancy in neural network?
- 2 Can neural network solve any problem?
- 3 Which problems are appropriate for neural network?
- 4 What is overfitting in neural network training which are the two approaches to avoid overfitting?
- 5 Why does dropout prevent overfitting?
- 6 Which issues can be faced at training decision tree?
- 7 Is termed as universal Approximator?
- 8 Why recurrent neural networks are hard to train?
- 9 How to improve the loss function of neural network?
What is redundancy in neural network?
One biological principle that is often overlooked in the design of artificial neural networks (ANNs) is redundancy. Redundancy is the replication of processes within the brain.
Can neural network solve any problem?
A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly. If you accept most classes of problems can be reduced to functions, this statement implies a neural network can, in theory, solve any problem.
Which problems are appropriate for neural network?
Appropriate Problems for ANN
- training data is noisy, complex sensor data.
- also problems where symbolic algos are used (decision tree learning (DTL)) – ANN and DTL produce results of comparable accuracy.
- instances are attribute-value pairs, attributes may be highly correlated or independent, values can be any real value.
Why neural networks are universal function Approximators?
The Universal Approximation Theorem tells us that Neural Networks has a kind of universality i.e. no matter what f(x) is, there is a network that can approximately approach the result and do the job! This result holds for any number of inputs and outputs. Non-linearities help Neural Networks perform more complex tasks.
Can neural network learn anything?
‘ Having said that, yes, a neural network can ‘learn’ from experience. In fact, the most common application of neural networks is to ‘train’ a neural network to produce a specific pattern as its output when it is presented with a given pattern as its input.
What is overfitting in neural network training which are the two approaches to avoid overfitting?
I followed it up by presenting five of the most common ways to prevent overfitting while training neural networks — simplifying the model, early stopping, data augmentation, regularization and dropouts.
Why does dropout prevent overfitting?
Dropout prevents overfitting due to a layer’s “over-reliance” on a few of its inputs. Because these inputs aren’t always present during training (i.e. they are dropped at random), the layer learns to use all of its inputs, improving generalization.
Which issues can be faced at training decision tree?
Issues in Decision Tree Learning
- Overfitting the data:
- Guarding against bad attribute choices:
- Handling continuous valued attributes:
- Handling missing attribute values:
- Handling attributes with differing costs:
What is neural network and how it solves problems?
What are neural networks? Artificial neural networks are a form of machine-learning algorithm with a structure roughly based on that of the human brain. Like other kinds of machine-learning algorithms, they can solve problems through trial and error without being explicitly programmed with rules to follow.
Can ReLU approximate any function?
We have proved that a sufficiently large neural network using the ReLU activation function can approximate any function in L^1 up to any arbitrary precision.
Is termed as universal Approximator?
Such a set of spanning functions, of which there are infinitely many varieties, can approximate every function universally, and is thus often referred to as a universal approximator. This notion of universal approximation of functions is illustrated in the right panel of Figure 11.10.
Why recurrent neural networks are hard to train?
Recurrent Neural Networks enable you to model time-dependent and sequential data problems, such as stock market prediction, machine translation, and text generation. You will find, however, that recurrent Neural Networks are hard to train because of the gradient problem. RNNs suffer from the problem of vanishing gradients.
How to improve the loss function of neural network?
In the process of training, we want to start with a bad performing neural network and wind up with network with high accuracy. In terms of loss function, we want our loss function to much lower in the end of training. Improving the network is possible, because we can change its function by adjusting weights.
What is a good number range for neural network output?
The last thing to note, is that we usually want a number between 0 and 1 as an output from out neural network so that we treat is as a probability. For example, in dogs-vs-cats we could treat a number close to zero as a cat, and a number close to one as a dog.
Why should we normalize the input of neural networks?
Another reason that recommends input normalization is related to the gradient problem we mentioned in the previous section. The rescaling of the input within small ranges gives rise to even small weight values in general, and this makes the output of the units of the network near the saturation regions of the activation functions less likely.