Intro

Question 1

Overfitting

Answer 1

the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably

Question 2

Underfitting

Answer 2

a data model is unable to capture the relationship between the input and output variables accurately, generating a high error rate on both the training set and unseen data

Question 3

Neural network

Answer 3

a biologically inspired mathematical function made up of artifical “neurons” that interact with one another. Neurons are typically represented by circles and arrows between neurons represent weights that describe the relationships between the neurons

Question 4

Sigmoid function

Answer 4

A weighted sum of inputs is passed through an activation function and this output serves as an input to the next layer. When the activation function for a neuron is a sigmoid function it is a guarantee that the output of this unit will always be between 0 and 1

Question 5

Recurrent neural networks (RNN)

Answer 5

a neural network made up of identical “units” of neurons that feed into one another in a series. The most common type of network that interacts with time series or sequential data

Question 6

Long Short-Term Memory (LSTM) networks

Answer 6

variant of RNNs. Its Gating mechanism sets it apart. This feature addresses the short term memory problem of RNNs

Question 7

Regularization

Answer 7

step taken to reduce Overfitting (high variance) and underfitting (high bias)

Question 8

Tensor

Answer 8

mathematical objects that can be used to describe physical properties, just like scalars and vectors. In fact tensors are merely a generalization of scalars and vectors; a scalar is a zero rank tensor, and a vector is a first rank tensor.

Question 9

Scalar

Answer 9

has only magnitude, no direction

Question 10

Vector

Answer 10

having magnitude and direction

Question 11

Backpropagation

Answer 11

short for “backward propagation of errors,” is an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the method calculates the gradient of the error function with respect to the neural network’s weights.

Question 12

Gradient descent

Answer 12

an iterative first-order optimization algorithm used to find a local minimum/maximum of a given function. This method is commonly used in machine learning (ML) and deep learning(DL) to minimize a cost/loss function (e.g. in a linear regression)

Question 13

Gradient

Answer 13

calculated by taking the partial derivative of a function with respect to each variable. Result is expressed as a vector

Question 14

Jacobian matrix

Answer 14

the Jacobian matrix of a vector-valued function of several variables is the matrix of all its first-order partial derivatives.

Question 15

Normalization

Answer 15

The goal of normalization is to transform features to be on a similar scale. This improves the performance and training stability of the model

Question 16

Data lake

Answer 16

datastore where data is stores from multiple datasources. Typically dirty with missing fields and inconsistent data types. Can have schema applied and data permissioning

Question 17

Data pipeline

Answer 17

Retrieve data from datasource (ingestion) -> apply data lake schema/permissioning parameters -> store in data lake (aggregation of data stores) -> pull data from data lake and clean/structure it (feature engineering)

Question 18

Imputation

Answer 18

filling in missing data values

Question 19

Data warehouse

Answer 19

different from data lake. Take slice of historical data, put data in column format as opposed to row, and apply additional transformations. Proxy between data lake and analyst. Aggregations are faster on column structured data

Question 20

Confusion matrix

Answer 20

visualize false positives, false negatives, etc. Uses to grade your ML algorithm, not train

Question 21

Classifier

Answer 21

In data science, a classifier isa type of machine learning algorithm used to assign a class label to a data input. An example is an image recognition classifier to label an image (e.g., “car,” “truck,” or “person”).

Question 22

Hyperparameter

Answer 22

In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters are derived via training.

Question 23

Matrix factorization

Answer 23

Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices.

Question 24

Convolutional layer

Answer 24

A convolutional layer isthe main building block of a CNN. It contains a set of filters (or kernels), parameters of which are to be learned throughout the training. The size of the filters is usually smaller than the actual image

Question 25

Examples of machine learning use cases

Answer 25

Recommender systems
Sentiment analysis
Classification/Clustering
Spam detection
Anomaly detection

Question 26

Support Vector Machines

Answer 26

Supervised learning algorithms that analyze data for classification and regression analysis.

A simple example may be drawing a lime on a graph where the points above the line are one category and those below the line are another category.

Question 27

Hyperplane

Answer 27

subspace whose dimension is one less than that of its ambient space. For example, if a space is 3-dimensional then its hyperplanes are the 2-dimensional planes, while if the space is 2-dimensional, its hyperplanes are the 1-dimensional lines.

Question 28

Linear regression

Answer 28

Simple regression machine learning algorithm that predicts your response variable y based on one or more independent variables X, fitting a linear relationship between the two.

Question 29

Cost function

Answer 29

Function used to provide a quantitative measure of a machine learning algorithms prediction inaccuracy. Mean squared error is a commonly used example

Question 30

Learning curves

Answer 30

A good tool to understand whether overfitting is going on.