Index

A

• activation function / A logistic regressor
• active learning
  • about / Using less data – active learning
  • labeling budgets, using / Using labeling budgets efficiently
  • leverage machines, for human labeling / Leveraging machines for human labeling
  • pseudo labeling, for unlabelled data / Pseudo labeling for unlabeled data
  • generative models, using / Using generative models
• activity_regularizer / Regularization in Keras
• adam (adaptive momentum estimation) / The Adam optimizer
• adam optimizer / The Adam optimizer
• advantage actor-critic (A2C) model
  • about / Advantage actor-critic models
  • pendulum, balancing / Learning to balance
  • trading / Learning to trade
• aggregate global feature statistics / Aggregate global feature statistics
• Amazon Mechanical Turk (MTurk) / Fine-tuning the NER
• Anaconda
  • reference / Running notebooks locally
• anti-discrimination law
  • disparate impact / Legal perspectives
• approaches, beyond image classification
  • about / Computer vision beyond classification
  • facial recognition / Facial recognition
  • bounding box prediction / Bounding box prediction
• asynchronous advantage actor-critic (A3C) / Advantage actor-critic models
• attention mechanism
  • about / Attention
• auto-sklearn
  • reference / Learning how to learn
• autocorrelation / Autocorrelation
• autoencoders
  • about / Understanding autoencoders
  • for MNIST / Autoencoder for MNIST
  • for credit cards / Autoencoder for credit cards
• Automatic Differentiation Variational Inference (AVI) / From probabilistic programming to deep probabilistic programming
• autoregression / ARIMA
• Autoregressive Integrated Moving Average (AIRMA)
  • about / ARIMA
• AutoWEKA
  • reference / Learning how to learn
• AWS deep learning AMI
  • reference / Using the AWS deep learning AMI
  • using / Using the AWS deep learning AMI

B

• backtesting
  • about / A note on backtesting
  • biases / A note on backtesting
• bag of words classification / Bag-of-words
• batchnorm
  • about / Batchnorm
• Bayesian deep learning / Bayesian deep learning
• Bayesian inference
  • about / An intuitive guide to Bayesian inference
  • flat prior / Flat prior
  • < 50% prior / <50% prior
  • prior / Prior and posterior
  • posterior / Prior and posterior
  • Markov Chain Monte Carlo / Markov Chain Monte Carlo
  • stochastic volatility example / Metropolis-Hastings MCMC
  • probabilistic programming, migrating to deep probabilistic programming / From probabilistic programming to deep probabilistic programming
• behavioral economics / Understanding the brain through RL
• Bellman equation
  • about / Markov processes and the bellman equation – A more formal introduction to RL, The Bellman equation in economics
• biases, backtesting
  • look-Ahead bias / A note on backtesting
  • survivorship biasTopicn / A note on backtesting
  • psychological tolerance bias / A note on backtesting
  • overfitting / A note on backtesting
• bias_regularizer / Regularization in Keras
• bounding box prediction
  • about / Bounding box prediction
  • YOLO approach / Bounding box prediction
• building blocks, ConvNets in Keras
  • Conv2D / Conv2D
  • padding / Padding
  • input shape / Input shape
  • MaxPooling2D / MaxPooling2D
  • flatten operation / Flatten
  • dense alyers / Dense

C

• catastrophes / Catastrophes are caused by multiple failures
• Catch
  • about / Catch – a quick guide to reinforcement learning
  • playing / Training to play Catch
• categorical data / Preparing the data for the Keras library
• causal learning
  • about / Causal learning
  • causal models, obtaining / Obtaining causal models
  • instrument variables / Instrument variables
  • nonlinear causal models / Non-linear causal models
• complex system failure
  • unfairness approach / Unfairness as complex system failure
• complex systems
  • disadvantages / Complex systems are intrinsically hazardous systems
  • executing, in degraded mode / Complex systems run in degraded mode
• computers / Our journey in this book
• confusion matrix
  • used, for evaluating heuristic model / Evaluating with a confusion matrix
• Conv1D / Conv1D
• Conv2D
  • about / Conv2D
  • kernel size / Kernel size
  • stride size / Stride size
  • padding / Padding
  • input shape / Input shape
  • ReLu activation / ReLU activation
• ConvNet
  • building blocks, in Keras / The building blocks of ConvNets in Keras
  • training, on MNIST / Training MNIST
  • MNIST model / The model
  • MNIST dataset, loading / Loading the data
  • MNIST dataset, compiling / Compiling and training
  • MNIST dataset, training / Compiling and training
• ConvNets
  • about / Convolutional Neural Networks
  • filters, on MNIST / Filters on MNIST
  • second filter, adding / Adding a second filter
• convolve operation
  • using / Examining the sample time series
• count vector / Bag-of-words
• covariance stationarity
  • about / Different kinds of stationarity
• CUDA
  • reference / Installing TensorFlow
• Cython documentation
  • reference / Speeding up your code with Cython

D

• data
  • preparing / Preparing the data
• data, Seq2Seq models
  • about / The data
  • characters, encoding / Encoding characters
• data debugging
  • about / Debugging data
  • task eligibility, checking / How to find out whether your data is up to the task
  • rules / How to find out whether your data is up to the task
  • enough data situations / What to do if you don't have enough data
  • unit testing / Unit testing data
  • privacy, maintaining / Keeping data private and complying with regulations
  • best practices / Keeping data private and complying with regulations
  • preparation, for training / Preparing the data for training
  • inputs, comparing to predictions / Understanding which inputs led to which predictions
• data preparation
  • characters, sanitizing / Sanitizing characters
  • lemmatization / Lemmatization
  • target, preparing / Preparing the target
  • train, preparing / Preparing the training and test sets
  • test set, preparing / Preparing the training and test sets
• dataset / The data
• Dataset API
  • reference / Optimizing your pipeline
• data trap / The feature engineering approach
• deeper network
  • creating / A deeper network
• deep learning
  • shortcoming / Learning how to learn
• deep neural networks / All models are wrong
• deployment
  • about / Deployment
  • product launch / Launching fast
  • metrics, monitoring / Understanding and monitoring metrics
  • data origin / Understanding where your data comes from
• dilated and causal convolution / Dilated and causal convolution
• discrete Fourier transform (DFT) / Fast Fourier transformations
• disparate sample size / Sources of unfairness in machine learning
• dropout
  • about / Dropout
• dummy variable / One-hot encoding

E

• end-to-end (E2E) modeling
  • about / E2E modeling
• end-to-end models / Heuristic, feature-based, and E2E models
• entity embeddings
  • about / Entity embeddings
  • categories, tokenizing / Tokenizing categories
  • input models, creating / Creating input models
  • model, training / Training the model
• evolutionary strategies (ES) / Evolutionary strategies and genetic algorithms

F

• 2010 Flash Crash use case / VAEs for time series
• fair models
  • developing, checklist / A checklist for developing fair models, Is the data biased?
• false negatives (FN) / Observational fairness
• false positives (FP) / Observational fairness
• Fast Fourier transformations / Fast Fourier transformations
• feature-based models / Heuristic, feature-based, and E2E models
• feature engineering approach
  • about / The feature engineering approach
  • fraudsters / A feature from intuition – fraudsters don't sleep
  • fraudulent transfer destination / Expert insight – transfer, then cash out
  • fraudulent cash outs / Expert insight – transfer, then cash out
  • balance errors / Statistical quirks – errors in balances
• feature scaling, ways
  • standardization / Preparing the data for training
  • Min-Max rescaling / Preparing the data for training
  • mean normalization / Preparing the data for training
  • unit length scaling, applying / Preparing the data for training
• filters
  • applying, on color images / Filters on color images
• forecasting, with neural nets
  • about / Forecasting with neural networks
  • data preparation / Data preparation
  • data preparation, weekdays / Weekdays
• forward pass / A forward pass
• four-fifths rule / Legal perspectives
• fraud detection
  • SGAN, using / SGANs for fraud detection
• frontiers, RL
  • about / Frontiers of RL, Understanding the brain through RL
  • multi agents / Multi-agent RL
  • many agents / Multi-agent RL
  • many / Multi-agent RL
• function approximators / Approximating functions
• functions
  • approximating / Approximating functions

G

• GANs
  • about / GANs
  • training process / GANs
  • MNIST GAN / A MNIST GAN
  • latent vectors / Understanding GAN latent vectors
  • training tricks / GAN training tricks
• General Data Protection Regulation (GDPR) / Keeping data private and complying with regulations
• generative models
  • using / Using generative models
• genetic algorithms / Evolutionary strategies and genetic algorithms
• global features
  • about / Visualization and preparation in pandas
  • issues / Aggregate global feature statistics
• Global Vectors (GloVe) / Loading pretrained word vectors
• Google cloud AutoML
  • reference / Learning how to learn
• graphics processing units (GPUs) / Using the right hardware for your problem
• Graphics Processing Units (GPUs) / Setting up your workspace
• Gym
  • reference / Learning to balance

H

• H2O AutoML
  • reference / Learning how to learn
• heuristic model
  • about / Heuristic, feature-based, and E2E models, The heuristic approach
  • used, for making predictions / Making predictions using the heuristic model
  • F1 score / The F1 score
  • evaluating, with confusion matrix / Evaluating with a confusion matrix
• hyper-parameters / Gradient descent
• hyperas
  • used, for searching hyperparameter / Hyperparameter search with Hyperas
  • reference / Hyperparameter search with Hyperas
  • installation adjustments / Hyperparameter search with Hyperas
• Hyperopt
  • reference / Learning how to learn
• hyperparameter
  • searching, with hyperas / Hyperparameter search with Hyperas

I

• image datasets
  • working with / Working with big image datasets
• instrumental variables two-stage least squares (IV2SLS) / Instrument variables
• integrated / ARIMA

J

• JobLib
  • reference / Flat prior

K

• Kaggle
  • reference / Using Kaggle kernels, The data, An introductory guide to spaCy
• Kaggle Kernel demoing marbles
  • reference / Unit testing data
• Kalman filters / Kalman filters
• Keras
  • about / A brief introduction to Keras
  • importing / Importing Keras
  • two-layer model / A two-layer model in Keras
  • and TensorFlow / Keras and TensorFlow
  • used, for creating predictive models / Creating predictive models with Keras
  • building blocks, ConvNets / The building blocks of ConvNets in Keras
  • documentation, reference / Augmentation with ImageDataGenerator
• Keras functional API
  • about / A quick tour of the Keras functional API
• Keras library
  • data, preparing / Preparing the data for the Keras library
  • nominal data / Preparing the data for the Keras library
  • ordinal data / Preparing the data for the Keras library
  • numerical data / Preparing the data for the Keras library
  • one-hot encoding / One-hot encoding
  • entity embeddings / Entity embeddings
• Keras library
  • one-hot encoding / One-hot encoding
• kernel_regularizer / Regularization in Keras
• Kullback-Leibler (KL) divergence / Visualizing latent spaces with t-SNE

L

• Latent Dirichlet Allocation (LDA) / Topic modeling
• latent spaces
  • visualizing, with t-SNE / Visualizing latent spaces with t-SNE
• learning rate / Parameter updates
• linear step / A logistic regressor
• Local Interpretable Model-Agnostic Explanations (LIME) / Understanding which inputs led to which predictions
• logistic regressor
  • about / A logistic regressor
  • Python version / Python version of our logistic regressor
• LSTM
  • about / LSTM
  • carry / The carry

M

• machine learning / What is machine learning?
• marbles
  • reference / Unit testing data
• Markov Chains
  • reference / Markov processes and the bellman equation – A more formal introduction to RL
• Markov processes / Markov processes and the bellman equation – A more formal introduction to RL
• matrix multiplication (matmul) / Tensors and the computational graph
• mean absolute percentage (MAPE) / Establishing a training and testing regime
• mean stationarity
  • about / Different kinds of stationarity
• median forecasting / Median forecasting
• ML
  • unfairnes, sources / Sources of unfairness in machine learning
• ML software stack
  • Keras / The machine learning software stack
  • NumPy / The machine learning software stack
  • Pandas / The machine learning software stack
  • Scikit-learn / The machine learning software stack
  • Matplotlib / The machine learning software stack
  • Jupyter / The machine learning software stack
  • about / The machine learning software stack
• MNIST
  • filters / Filters on MNIST
• MNIST Autoencoder VAE
  • reference / Autoencoder for MNIST
• model debugging
  • about / Debugging your model
  • hyperas, used for searching hyperparameter / Hyperparameter search with Hyperas
  • learning rate, searching / Efficient learning rate search
  • learning rate, scheduling / Learning rate scheduling
  • TensorBoard, used for training monitoring / Monitoring training with TensorBoard
  • vanishing gradient problem / Exploding and vanishing gradients
  • exploding gradient problem / Exploding and vanishing gradients
• model loss
  • measuring / Measuring model loss
  • gradient descent / Gradient descent
  • backpropagation / Backpropagation
  • parameter updates / Parameter updates
  • 1-layer neural network, training / Putting it all together
• model parameters
  • optimizing / Optimizing model parameters
• models
  • training, for maintaining fairness measures / Training to be fair
  • interpreting, for ensuring fairness / Interpreting models to ensure fairness
  • inspecting, for unfairness / Unfairness as complex system failure, Complex systems run in degraded mode, Accident-free operation requires experience with failure
• modularity trade-off / The modularity tradeoff
• momentum / Momentum
• Moving Average / ARIMA

N

• named entity recognition (NER)
  • about / Named entity recognition
  • fine tuning / Fine-tuning the NER
• neural nets
  • used, for forecasting / Forecasting with neural networks
  • uncertainty / Bayesian deep learning
• neural networks (NNs) / Approximating functions
• No-U-Turn Sampler (NUTS) / Metropolis-Hastings MCMC
• nonlinear causal models / Non-linear causal models
• nonlinear step / A logistic regressor
• notebook, executing
  • TensorFlow, installing / Installing TensorFlow
  • Keras, installing / Installing Keras

O

• observational fairness
  • about / Observational fairness
• off the shelf AutoML solutions
  • tpot / Learning how to learn
  • auto-sklearn / Learning how to learn
  • AutoWEKA / Learning how to learn
  • H2O AutoML / Learning how to learn
  • Google cloud AutoML / Learning how to learn
• one-hot encoding / One-hot encoding
• overfitting / Creating a test set

P

• pandas
  • visualization / Visualization and preparation in pandas
  • preparation / Visualization and preparation in pandas
  • reference / Named entity recognition
• part of speech (POS) tagging
  • about / Part-of-speech (POS) tagging
• performance tips, machine learning applications
  • about / Performance tips
  • right hardware , using / Using the right hardware for your problem
  • distributed training, using with TF estimators / Making use of distributed training with TF estimators
  • CuDNNLSTM, using / Using optimized layers such as CuDNNLSTM
  • pipeline, optimizing / Optimizing your pipeline
  • Cython, used for speeding up code / Speeding up your code with Cython
  • cache frequent requests / Caching frequent requests
• predictive models, creating
  • training data, oversampling / Oversampling the training data
• predictive models, creating with Keras
  • about / Creating predictive models with Keras
  • target, extracting / Extracting the target
  • test set, creating / Creating a test set
  • building / Building the model
  • simple baseline, creating / Creating a simple baseline
  • complex models, building / Building more complex models
• preexisting social biases / Sources of unfairness in machine learning
• pretrained models
  • working with / Working with pretrained models
  • VGG16, modifying / Modifying VGG-16
  • random image augmentation / Random image augmentation
  • random image augmentation, with ImageDataGenerator / Augmentation with ImageDataGenerator
• pretrained word vectors
  • loading / Loading pretrained word vectors
• principal component analysis (PCA) / Understanding autoencoders
• Python
  • regex module, using / Using Python's regex module

Q

• Q-function / Catch – a quick guide to reinforcement learning
• Q-learning
  • about / Catch – a quick guide to reinforcement learning
  • used, for conversion of RL into supervised learning / Q-learning turns RL into supervised learning
  • exploration / Training to play Catch
• Q-learning model
  • defining / Defining the Q-learning model
• qualitative rationale / The feature engineering approach

R

• recurrent dropout / Recurrent dropout
• recurrent neural networks (RNN) / Simple RNN
• regex module
  • using, in Python / Using Python's regex module
  • using, in pandas / Regex in pandas
  • using / When to use regexes and when not to
• Region-based Convolutional Neural Network (R-CNN) / Bounding box prediction
• regular expressions
  • about / Regular expressions
• regularization
  • about / Regularization
  • L2 regularization / L2 regularization
  • L1 regularization / L1 regularization
  • in Keras / Regularization in Keras
• reinforcement learning
  • about / Reinforcement learning
  • effectiveness of data / The unreasonable effectiveness of data
  • machine learning models / All models are wrong
  • conversion, to supervised learning with Q-learning / Q-learning turns RL into supervised learning
• reinforcement learning (RL)
  • about / Catch – a quick guide to reinforcement learning, Markov processes and the bellman equation – A more formal introduction to RL
  • frontiers / Frontiers of RL
• reward functions, designing
  • manual reward shaping / Careful, manual reward shaping
  • inverse reinforcement learning (IRL) / Inverse reinforcement learning
  • human preferences, learning / Learning from human preferences
  • robost RL, creating / Robust RL
• RL engineering
  • best practices / Designing good reward functions
  • reward functions, designing / Designing good reward functions
• rule-based matching
  • about / Rule-based matching
  • custom functions, adding to matchers / Adding custom functions to matchers
  • matchers, adding to pipeline / Adding the matcher to the pipeline
  • combining, with learning based systems / Combining rule-based and learning-based systems

S

• sampling biases / Sources of unfairness in machine learning
• semi-supervised generative adversarial network (SGAN)
  • about / Using generative models
  • used, for fraud detection / SGANs for fraud detection
  • reference / SGANs for fraud detection
• semi-supervised learning / Using less data – active learning
• Seq2Seq models
  • about / Seq2seq models
  • architecture overview / Seq2seq architecture overview
  • data / The data
  • inference models, creating / Creating inference models
  • translations, creating / Making translations, Exercises
• SHAP (SHapley Additive exPlanation) / Interpreting models to ensure fairness
• simple model / What to do if you don't have enough data
• simple RNN / Simple RNN
• SpaCy
  • about / An introductory guide to spaCy, Document similarity with word embeddings
  • Doc instance / An introductory guide to spaCy
  • Vocab class / An introductory guide to spaCy
• Spearmint
  • reference / Learning how to learn
• stationarity
  • types / Different kinds of stationarity
  • significance / Why stationarity matters
• stationarity issues
  • avoiding / When to ignore stationarity issues
• Stochastic Gradient Descent (SGD) / Compiling the model
• stochastic volatility
  • reference / Metropolis-Hastings MCMC
• supervised learning / Supervised learning, Using less data – active learning
• Synthetic Minority Over-sampling Technique (SMOTE) / Oversampling the training data
• systematic error / Sources of unfairness in machine learning

T

• t-SNE algorithm
  • used, for visualizing latent spaces / Visualizing latent spaces with t-SNE
• Tabotea project / The data
• tensors
  • and computational graph / Tensors and the computational graph
• Term Frequency, Inverse Document Frequency (TF-IDF) / TF-IDF
• test set / Creating a test set
• text classification task
  • about / A text classification task
• time series
  • examining / Examining the sample time series
• time series models
  • using, with word vectors / Time series models with word vectors
• time series stationary
  • making / Making a time series stationary
• topic modeling / Topic modeling
• tpot
  • reference / Learning how to learn
• training and testing regime
  • establishing / Establishing a training and testing regime
• transfer learning / What to do if you don't have enough data
• tree-based methods
  • about / A brief primer on tree-based methods
  • decision tree / A simple decision tree
  • random forest / A random forest
  • XGBoost / XGBoost
• Tree of Parzen (TPE) algorithm / Hyperparameter search with Hyperas
• true negatives (TN) / Observational fairness
• true payoff probability (TPP) / An intuitive guide to Bayesian inference
• true positives (TP) / Observational fairness
• two-layer model, Keras
  • about / A two-layer model in Keras
  • layer, stacking / Stacking layers
  • model, compiling / Compiling the model
  • model, training / Training the model

U

• unsupervised learning / Unsupervised learning, Using less data – active learning

V

• vanishing gradient problem / Monitoring training with TensorBoard
• variance stationarity
  • about / Different kinds of stationarity
• variational autoencoders (VAEs)
  • about / Variational autoencoders
  • MNIST example / MNIST example
  • Lambda layer, using / Using the Lambda layer
  • Kullback-Leibler divergence / Kullback–Leibler divergence
  • custom loss, creating / Creating a custom loss
  • using, for data generation / Using a VAE to generate data
  • used, for end-to-end fraud detection / VAEs for an end-to-end fraud detection system
  • using, in time series / VAEs for time series

W

• word embeddings
  • about / Word embeddings
  • document similarity / Document similarity with word embeddings
• word vectors
  • used, for preprocessing / Preprocessing for training with word vectors
• workspace
  • setting up / Setting up your workspace, Using Kaggle kernels
  • notebooks, local execution / Running notebooks locally

X

• Xtreme Gradient Boosting (XGBoost)
  • reference / A brief primer on tree-based methods, XGBoost
  • about / XGBoost

Y

• You Only Look Once (YOLO) / Bounding box prediction