karimkhanp

About Perplexity - https://planspace.org/2013/09/23/perplexity-what-it-is-and-what-yours-is/

karimkhanp

https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-interview-questions
https://www.javatpoint.com/deep-learning-interview-questions

Difference between training, dev and test set
    A training dataset is a dataset of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier.[7][8]
    Dev/Validation : A validation dataset is a dataset of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set". An example of a hyperparameter for artificial neural networks includes the number of hidden units in each layer.
    A test dataset is a dataset that is independent of the training dataset, but that follows the same probability distribution as the training dataset.

What is bias?
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Model with high bias pays very little attent

karimkhanp

import sys, pdb
import nltk, pprint
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nlp_opn import CuriaNLP
from mongo_op import MongoOperation

"""
NltkSentTokenize Class for all nltk sent tokenize
"""

karimkhanp

Regression: (https://www.quora.com/What-is-regression)
      Regression is the dependence of one variable over the other variable is termed as “Regression”. the statistical method which helps us to estimate the unknown value of one variable (dependent variable) from the known value of the related variable (independent variable) is called Regession

      Regression estimates the relationship among variables for prediction.
      Regression analysis helps to understand how the dependent variable changes when some of the independent variables are varied, while the other independent variables are held fixed.
      It determines the relationship between one dependent variable and a number of other independent variables.
      
Linear Regression
       A  Simple Linear Regression allows you to determine functional dependency  between two sets of numbers. For example, we can use regression to  determine the relation between ice cream sales and average temperature. 
       

karimkhanp

import sys

"""
NltkSentTokenize Class for all nltk sent tokenize
"""
class NltkSentTokenize(object):
    """
    Initialization function of NltkSentTokenize Class
    """
    def __init__(self):

karimkhanp

Links

http://bridgei2i.com/ebook/churn-propensity-model/#page/8

karimkhanp

karimkhanp

karimkhanp

What is Chatbot?
Types of Chatbot - Open domain vs Closed domain, Rules based vs General AI
Different approaches to build chatbots
	Existing frameworks
	Machine learning and NLP Based 
	AIML (Artificial Intelligence Markup Language)
How does each approach works.
IBM Watson, The most intelligent chatbot - Introduction 
Modules to build open domain chatbot using NLP and Machine learning
	Question Analysis

karimkhanp

What is difference between SVM and Neural Network? Is it true that linear svm is same NN, and for non-linear separable problems, NN uses adding hidden layers and SVM uses changing space dimensions?

There are two parts to this question. The first part is "what is the form of function learned by these methods?" For NN and SVM this is typically the same. For example, a single hidden layer neural network uses exactly the same form of model as an SVM. That is:

Given an input vector x, the output is: output(x) = sum_over_all_i weight_i * nonlinear_function_i(x)

Generally the nonlinear functions will also have some parameters. So these methods need to learn how many nonlinear functions should be used, what their parameters are, and what the value of all the weight_i weights should be.

Therefore, the difference between a SVM and a NN is in how they decide what these parameters should be set to. Usually when someone says they are using a neural network they mean they are trying to find the parameters which minimiz