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Ans. The commonly used loss functions are Linear : g(x) = x. This is the simplest activation function. However it cannot model complex decision boundaries. A deep network with linear activations can be shown incapable of handling non-linear decision boundaries. Sigmoid : This is a common activation function in the last layer of the neural…
Increase the number of training iterations Make a more complex network – increase hidden layer size Initialize weights to a random small value instead of zeros Change tanh activations to relu Ans : (3) . I will initialize weights to a non zero value since changing all the weights in the same…
L2 norm regularization : Make the weights closer to zero prevent overfitting. L1 Norm regularization : Make the weights closer to zero and also induce sparsity in weights. Less common form of regularization Dropout regularization : Ensure some of the hidden units are dropped out at random to ensure the network does not overfit by…
Increase number of units in the hidden layer Increase number of hidden layers Increase data set size Change activation function to tanh Try all of the above The answer is d. When I use a linear activation function, the deep neural network is realizing a linear combination of linear functions which leads to modeling only…
High bias means the data is being underfit. The decision boundary is not usually complex enough. High variance happens due to over fitting, the decision boundary is more complex than what it should be. High bias high variance happens when you fit a complex decision boundary that is also not fitting the training set…
The KNN algorithm is commonly used in many ML applications – right from supervised settings such as classification and regression, to just retrieving similar items in applications such as recommendation systems, search, question answering and so on. What is the KNN Algorithm? KNN for Nearest Neighbour Search: KNN algorithm involves retrieving the K datapoints that are…
This is typically called topic modeling. Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. For instance, two statements – about meals and about food can probably characterized by the same topic though they do not necessarily use the same vocabulary. Topic models typically…
Speaker diarization or speaker segmentation is the process of automatically assigning a speaker identity to each segment of the audio file. Segmenting by speaker is very useful in several applications to understand who said what in a conversation. Typically speaker information is crucial for applications such as emotion detection, behavioural analysis or topic analysis of…
A language model is a probability distribution over sequences of words P(w_1,… ,w_m). It enables us to measure the relative likelihood of different phrases. Measuring the likelihood of a sequence of words is useful in many NLP tasks such as speech recognition, machine translation, POS tagging, parsing, and so on. Example : In any generative…
Notable NER platforms include: GATE supports NER across many languages and domains out of the box, usable via a graphical interface and a Java API. OpenNLP includes rule-based and statistical named-entity recognition. SpaCy features fast statistical NER as well as an open-source named-entity visualizer.