Data Scientist Pocket Guide
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Description
Contents
Reviews
Language
English
ISBN
9789390684977
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. FAQ
How to fine tune a machine learning algorithm?
How to build deep neural network architecture?
How to train a machine learning algorithm faster?
Why do we normalize the input data in deep neural network?
When can we consider that we did a good job in a machine learning project?
When should we use deep learning instead of the traditional machine learning models?
How much time does it take to become a good data scientist?
How to evaluate the performance of a model?
In case of a large dataset, should I sample my data or use distributed computing?
How much time should I spend in data transformation?
How to select the right machine learning algorithm?
Should I learn R or Python?
What’s the trade-off between bias and variance?
What is the difference between supervised and unsupervised machine learning?
What is the difference between L1 and L2 regularizations?
What’s the difference between type I and type II error?
What’s the difference between probability and likelihood?
What’s the difference between a generative and discriminative model?
Which is more important model accuracy, or model performance?
How would you handle an imbalanced dataset?
How do you ensure that you’re not overfitting with a model?
What’s the “kernel trick” and how is it useful?
How do you handle missing data in a dataset?
What are the origins of machine learning?
What is the difference between a classifier and a model?
What is the difference between a parametric learning algorithm and a non-parametric learning algorithm?
What is the difference between a cost function and a loss function in machine learning?
What is the difference between covariance and correlation?
Why did it take so long for deep networks to be invented?
What are some good books/papers for learning machine learning?
What are the advantages of semi-supervised learning over supervised and unsupervised learning?
When should I apply data normalization/standardization?
How do you deal with a machine learning problem with a large number of features?
When should one use median as opposed to the mean or average?
Why is “Naive” Bayes called naive?
2. A
A/B testing
Accuracy
Action
Activation function
Active learning
AdaBoost
AdaDelta
AdaGrad
Adam
Adaptive learning rate
Affine layer
Agent
Agglomerative clustering
AlexNet
Algorithm
Anaconda
Anchor box
Annotator
ANOVA
Apache Spark
ARIMA
Artificial general intelligence (AGI)
Artificial intelligence
Artificial narrow intelligence (ANI)
Artificial super intelligence (ASI)
Association learning
Association rules
Attention mechanism
Attribute
Area under the ROC Curve (AUC)
Autocorrelation
Autoencoder
Automatic summarization
Automation bias
Autoregression
Average pooling
Average precision
3. B
Backpropagation
Backpropagation through time (BPTT)
Bag of words
Bagging
Bar chart
Base learner
Baseline
Batch
Batch gradient descent
Batch normalization
Bayes’ theorem
Bayesian inference
Bayesian statistics
Bellman equation
Bernoulli distribution
Bias
Bias-variance trade-off
Bidirectional Recurrent Neural Network
Big Data
Big O notation
Binarization
Binary classification
Binary variables
Binning
Binomial distribution
Black box model
BLEU score
Boosting
Bootstrapping
Bottleneck layer
Bounding box
Box plot
Bucketing
Business analytics
Business intelligence
4. C
Caffe
Calibration
Candidate generation
Candidate sampling
Categorical cross-entropy
Categorical variable
Centroid
Centroid-based algorithm
Chain rule
Chainer
Channel
Checkpoints
Chi-square test
Chi-squared distribution
CIFAR:
Classification
Classification threshold
Classifier
Clipping
Cloud
Clustering
CNN
CNTK
Co-adaptation
COCO
Coefficient of determination
Cohen’s kappa
Collaborative filtering
Complexity
Computer vision
Concordant-discordant ratio
Confidence interval
Confusion matrix
Connectivity-based algorithm
Continuous learning
Continuous variable
Contrastive divergence
Convenience sampling
Convergence
Convex function
Convolution
Convolutional layer
Convolutional neural network
Correlation
Cosine similarity
Cost function
Covariance
Coverage bias
CPU
Cross-entropy
Cross validation
CUDA
5. D
Dashboard
Data analysis
Data augmentation
Data engineering
Data mining
Data parallelism
Data preparation
Data science
Data transformation
Data wrangling
Database
Databricks
DataFrame
Dataset
Davies-Bouldin index
DBSCAN
Decile
Decision boundary
Decision tree
Deduction
Deep belief network
Deep dream
Deep learning
Deep Q-network
Deeplearning4j
Degree of freedom
Dense feature
Dense layer
Density-based algorithm
Dependent variable
Deployment as API
Deployment in batch
Depth
Depth-wise separable convolutional neural network
Descriptive statistics
Device
Dimensionality reduction
Discounted cumulative gain
Discrete variable
Discriminative model
Discriminator
Divisive clustering
Downpour stochastic gradient descent
Downsampling
Dplyr
DropConnect
Dropout regularization
Dummy variable
Dunn index
Dynamic model
Dynamic programming
6. E
Early stopping
EDA
ELU
Embedding space
Embeddings
Ensemble learning algorithm
Ensemble models:
Entropy
Episode
Epoch
Epsilon greedy policy
ETL
Euclidean distance
Evaluation metric
Example
Experimentation
Expert system
Exploding gradient problem
Exploration vs. exploitation
Exponential family distribution
Exponential loss
Exponential smoothing
Extrapolation
Extreme values
7. F
F1 Score
Face recognition
Facet
Factor analysis
False negative
False positive
Feature
Feature cross
Feature engineering
Feature hashing
Feature learning
Feature reduction
Feature selection:
Federated learning:
Feedback loop
Feedforward
Few-shot learning
Fine-tuning
Flume
Focal loss
Forget gate
Frechet inception distance
Frequentist statistics
F-score
Full softmax
Fully connected layer
8. G
Gain and Lift Charts
Gated Recurrent Unit (GRU)
Gaussian distribution
General AI
Generalization
Generalization curve
Generalized Linear Model (GLM)
Generative adversarial neural network (GAN)
Generative classification
Generator
Genetic algorithm
Ggplot2
Gini coefficient
GloVe
Go
Goodness of fit
GoogleNet
GPU
Gradient accumulation
Gradient descent
Greedy policy
Grid search
Ground truth
9. H
Hadoop
Hashing
Heuristic
Hidden layer
Hidden Markov model
Hierarchical clustering
Highway layer
Highway network
Hinge loss
Histogram
Hive
Holdout sample
Holt-Winters forecasting
Huber loss
Hyperparameter
Hyperparameter tuning
Hyperplane
Hypothesis
10. I
International Conference on Machine Learning (ICML)
Integrated Development Environment (IDE)
ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
Image recognition
ImageNet
Imbalanced dataset
Implicit bias
Imputation
Inception
Inception module
Independent and identically distributed (i.i.d.)
Independent Component Analysis (ICA)
Induction
Inferential statistics
Input gate
Input layer
Instance
Instance-based learning
Interpretability
Intersection over Union (IoU)
Intersection over Union (IoU)
Interquartile Range (IQR)
Item matrix
Iteration
11. J
Jacobian
Julia
Jupyter notebook
12. K
Keras
Kernel
Kernel support vector machine
KL divergence
K-means
K-median
K-nearest neighbors (kNN)
Kolmogorov Smirnov chart
Kurtosis:
13. L
L1 Loss
L1 regularization
L2 loss
L2 regularization
Labeled data
Lasso regression
Latent variable
Layer
Leaky ReLU
Learning rate
Least squares regression
Line chart
Linear activation function
Linear discriminant analysis
Linear model
Linear regression
Log loss
Log-Cosh loss
Logistic regression
Logits
Log-odds
Long Short-Term Memory (LSTM)
Loss curve
Loss function
Loss surface
14. M
Machine Learning
Machine translation
Magnet loss
Mahout
Majority class
Manhattan distance
MapReduce
Market basket analysis
Market mix modeling
Markov chain
Markov decision process
Markov property
Matplotlib
Matrix factorization
Max pooling
Maximum likelihood estimation
Mean
Mean absolute error
Mean reciprocal rank
Mean squared error
Median
Memory-based learning
Mini-batch
Mini-batch gradient descent
Minimax loss
Minority class
Management Information System (MIS)
Machine learning (ML)
ML-as-a-service (MLaaS)
MLOps
MNIST
Mode
Model capacity
Model parallelism
Model selection
Model
Momentum
Monte Carlo simulation
Moving average
Multi-agent reinforcement learning
Multi-class classification
Multilayer perceptron
Multinomial classification
Multivariate analysis
Multivariate regression
MXNet:
15. N
Naive Bayes
NaN
Nash equilibrium
Natural language generation
Natural language processing
Natural Language Understanding (NLU)
Negative class
Negative log likelihood
Nesterov accelerated gradient
Neural Machine Translation (NMT)
Neural network
Neural Turing machine (NTM)
Neuron
N-gram
No free lunch theorem
Node
Noise
Noise contrastive estimation
Nominal variable
Nonlinear transform function
Normal distribution
Normalization
Normalized discounted cumulative gain
NoSQL
Notebook
Null
Null accuracy:
Numerical data
Numpy
NVIDIA
16. O
Object Detection
Objective
Objective function
One hot encoding
One shot learning
One vs all
Online inference
Online learning
Oozie
OpenCV
Optimizer
Ordinal variable
Outlier
Output gate
Output layer
Overfitting
17. P
Pandas
Parallel processing
Parameter update
Parameters
Part of speech tagging
Partial derivative
Participation bias
Partitioning
Pattern recognition
Peak signal-to-noise ratio:
Perceptron:
Performance
Perplexity
Pie chart
Pig
Pipeline
Poisson distribution
Polynomial regression
Pooling
Population
Positive class:
Post-processing
Precision and recall
Prediction
Predictive model
Predictor variable
Pre-processing
Pre-trained model
Principal Component Analysis (PCA)
Prior belief
Probability density
Proxy label
P-value
Python
PyTorch
18. Q
Q-function
Q-learning
Quadratic loss
Quantile
Quantile loss
Quartile:
Question answering (NLP)
19. R
R
Radial basis function network
Random-Access Memory (RAM)
Random forest
Random initialization
Random policy
Random search
Range
Rank
Rater
Recommendation engine
Reconstruction entropy
Rectified linear unit
Recurrent neural network
Recursive neural network
Regression
Regression spline
Regularization
Reinforcement learning
Relationship extraction
Relative entropy
Rectified linear unit (ReLU)
Replay buffer
Representation
Representation learning
Residual
ResNet
Response variable
Restricted Boltzmann Machine (RBM)
Reward
Ridge regression
Ridge regularization
Risk
Root Mean Square Propagation (RMSProp)
Recurrent Neural Network (RNN)
Robotic Process Automation (RPA)
ROC-AUC
Root Mean Squared Error (RMSE)
Root Mean Squared Logarithmic Error (RMSLE)
Rotational invariance
R-squared/Adjusted R-squared
20. S
Sampling
Sampling bias
SAS
Scala
Scalar
Scaling
Scikit-learn
Scoring
Seasonality
Selection bias
Self-supervised learning
Semi-supervised learning
Sensitivity
Sentiment analysis
Sequence to sequence
Serialization
Shape of a tensor
Siamese neural network
Sigmoid function
Signal processing
Silhouette coefficient
Similarity learning
Single shot object detector
Singularity
Skewness
Skipgram
Smooth mean absolute error:
SMOTE
Softmax
Sparse feature
Sparse representation
Sparse vector
Sparsity
Spatial pooling
Spatial-temporal reasoning
Specificity
Speech recognition
Speech segmentation
Splitting data
SPSS
Structured Query Language (SQL)
Squared hinge loss
Squared loss
Stacking
Standard deviation
Standard error
Standardization
Stata
State
State-action value function
Static model
Stationary
Statistical inference
Statistics
STD decomposition:
Stochastic gradient descent
Stratified sampling
Stride
Strong AI
Strong classifier
Structural SIMilarity (SSIM)
Structured data
Subsampling
Supervised learning
Support vector machine (SVM)
SVM
Synthetic feature
21. T
Tanh
Target variable
T-distribution
Tensor
Tensorflow
Test set
Text-to-speech:
Theano
Time series analysis
Tokenization:
Topic modeling
Torch
Tensor Processing Unit (TPU)
Training
Training set
Translational invariance
Transfer learning
Transformer
Trend analysis
Triplet loss
True negative
True positive
Truncated SVD
T-test
Turing test
Type I error
Type II error
22. U
Underfitting
Univariate analysis
Universal function approximation theorem
Unlabeled data
Unstructured data
Unsupervised learning
Upweighting
User matrix
23. V
Validation set
Vanishing gradient problem
Variance
Variational autoencoder
VC dimension
Vector
VGG
24. W
Wasserstein loss
Watson studio
Weak classifier
Weight decay
Weight sharing
Weighted alternating least squares
Weighting
Width
Word embedding
Word segmentation
Word2vec
25. X
Xavier initialization
Xception
XGboost
26. Y
You only look once (YOLO)
27. Z
Zero shot learning
Z-test
Index
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