Index
A
- AI project
- problems, solving / The problem, goal, and business case
- goal / The problem, goal, and business case
- business case / The problem, goal, and business case
- AI workflow
- about / The AI workflow
- problem, characterizing / Characterize the problem, Checklist
- method, developing / Develop a method, Checklist
- deployment strategy, designing / Design a deployment strategy, Checklist
- continuous evaluation, designing / Design and implement a continuous evaluation, Checklist
- continuous evaluation, implementing / Design and implement a continuous evaluation, Checklist
- alternating least squares (ALS) / Deployment strategy
- Amazon Mechanical Turk / Method – sentiment analysis
- anomalies
- recognizing / Recognizing anomalies
- z-scores with static models / Z-scores with static models
- z-scores with sliding windows / Z-scores with sliding windows
- RPCA / RPCA
- clustering / Clustering
- Artificial Intelligence (AI)
- about / AI isn't everything
- artificial intelligence (AI)
- hype cycle / Understanding the hype cycle of AI
- Association for the Advancement of Artificial Intelligence (AAAI) / Develop a method
- autoregressive integrated moving average (ARIMA) / Discovering seasonal trends, ARIMA
B
- batch evaluation
- about / Continuous evaluation
- precision, calculating for BM25 weighting / Calculating precision and recall for BM25 weighting
- recall, calculating for BM25 weighting / Calculating precision and recall for BM25 weighting
- BM25 weighting / BM25 weighting
- precision, calculating / Calculating precision and recall for BM25 weighting
- recall, calculating / Calculating precision and recall for BM25 weighting
- business rules / Expert systems and business rules
C
- click-through rate (CTR) / Online evaluation of our recommendation system
- Cloud computing / The problem, goal, and business case
- cloud infrastructure
- deployment strategy / Deployment strategy
- evalution / Continuous evaluation
- cloud infrastructure planning
- problem / The problem, goal, and business case
- goal / The problem, goal, and business case
- business case / The problem, goal, and business case
- clustering technique / Clustering
- collaborative filtering recommendations
- about / Collaborative filtering recommendations
- BM25 weighting / BM25 weighting
- matrix factorization / Matrix factorization
- college course advising / A second example – college course advising
- Common Objects in Context (COCO) / YOLO and Darknet
- compressed sparse row matrix (CSR) / Calculating precision and recall for BM25 weighting
- computer vision / Computer vision
- confusion matrix / TensorFlow and Keras
- constraint solver
- about / Method – constraint solvers
- OptaPlanner / OptaPlanner
- content-based recommendations / Content-based recommendations
- convolutional neural networks (CNN) / Develop a method, The rise of machine learning
- convolutions / Convolutions
- CoreNLP
- reference link / Natural language processing
- CoreNLP processing pipeline / CoreNLP processing pipeline
- CoreNLP sentiment models
- retraining / Retraining CoreNLP sentiment models
- course advising domain / The course advising domain
- CPLEX
- reference link / Planning and scheduling
D
- Darknet
- using / YOLO and Darknet
- deep autoencoder / Clustering
- deep learning
- about / Neural networks and deep learning, Deep learning
- convolutions / Convolutions
- network architecture / Network architecture
- activation functions / Activation functions
- deployment strategy / Deployment strategy
- deployment strategy, sentiment analysis
- CoreNLP processing pipeline / CoreNLP processing pipeline
- Twitter API / Twitter API
- GATE platform / The GATE platform
- Reddit API / Reddit API
- News API / News API
- dashboard, with plotly.js / Dashboard with plotly.js and Dash
- dashboard, with Dash / Dashboard with plotly.js and Dash
- Drools
- reference link / Expert systems and business rules
- dynamic linear models (DLM) / Dynamic linear models
E
- EC2 (Elastic Compute Cloud) / The problem, goal, and business case
- expert systems / Expert systems and business rules
G
- Gafgyt
- reference / Recognizing anomalies
- GATE platform / The GATE platform
- Google Cloud Natural Language
- reference link / Natural language processing
- Google Cloud Translation
- reference link / Natural language processing
- Google Cloud Vision
- reference link / Computer vision
- Google Speech-to-Text
- reference link / Natural language processing
- Google Text-to-Speech
- reference link / Natural language processing
- graphics processing units (GPU) / Develop a method
- Gurobi
- reference link / Planning and scheduling
I
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC) / The rise of machine learning
- implicit feedback / Usage scenario – implicit feedback
- implicit library
- deployment strategy / Deployment strategy
- interactive AI systems
- problem / The problem, goal, and business case
- goal / The problem, goal, and business case
- business case / The problem, goal, and business case
J
- JSON (JavaScript Object Notation) / NLP with Rasa
K
- k-means clustering / Clustering
- Keras
- using / TensorFlow and Keras
L
- linear trends
- discovering / Discovering linear trends
- dynamic linear trends, discovering with sliding window / Discovering dynamic linear trends with a sliding window
- logic programming
- with Prolog / Logic programming with Prolog and tuProlog
- with tuProlog / Logic programming with Prolog and tuProlog
- logo detector
- continuous evaluation / Continuous evaluation
M
- machine learning (ML) / Characterize the problem
- about / The rise of machine learning
- matrix factorization / Matrix factorization
- max pooling / The rise of machine learning
- Mirai
- reference / Recognizing anomalies
- Mirai traffic
- reference / Clustering
N
- natural language generation (NLG)
- with Rasa / NLP with Rasa
- with SimpleNLG / Natural language generation with SimpleNLG
- natural language processing / Natural language processing
- natural language processing (NLP)
- with Rasa / NLP with Rasa
- neural network
- News API / News API
- NLP + logic programming + NLG
O
- online evaluation
- of recommendation system / Online evaluation of our recommendation system
- OpenCV
- reference link / Computer vision
- OptaPlanner / OptaPlanner
- reference link / Planning and scheduling
P
- Pan-STARRS1 (Panoramic Survey Telescope and Rapid Response System) / The problem, goal, and business case
- planning / Planning and scheduling
- Pokémon
- in Prolog / Pokémon in Prolog
- Pokémon domain / The Pokémon domain
- problems, constraint solver
- constraints / Method – constraint solvers
- entities, planning / Method – constraint solvers
- variables, planning / Method – constraint solvers
- solution, planning / Method – constraint solvers
- Prolog
- used, for logic programming / Logic programming with Prolog and tuProlog
- Pokemon / Pokémon in Prolog
- Prolog resolution / Prolog unification and resolution
- Prolog unification / Prolog unification and resolution
- pydlm library
- reference / Dynamic linear models
- pyramid library
- reference / ARIMA
- PyTorch
- reference link / Computer vision
R
- RA/Dec (right ascension, declination) / The problem, goal, and business case
- Rasa
- used, for natural language processing / NLP with Rasa
- reference link / Natural language processing
- rectified linear units (ReLUs) / The rise of machine learning
- recursive neural tensor networks (RNTN) / Method – sentiment analysis
- Reddit API / Reddit API
- robotics / Robotics
- Robot Operating System (ROS)
- reference link / Robotics
- robust principle component analysis (RPCA) / RPCA
- row-based linked-list sparse matrix (LIL) / Calculating precision and recall for BM25 weighting
- RPCA / RPCA
S
- scheduling / Planning and scheduling
- seasonal trends
- discovering / Discovering seasonal trends
- ARIMA / ARIMA
- dynamic linear models (DLM) / Dynamic linear models
- sentiment analysis
- about / Method – sentiment analysis
- deployment strategy / Deployment strategy
- continuous evaluation / Continuous evaluation
- SimpleNLG
- used, for natural language generation (NLG) / Natural language generation with SimpleNLG
- singular-value decomposition (SVD) / Content-based recommendations
- sliding window
- used, for discovering dynamic linear trends / Discovering dynamic linear trends with a sliding window
- social media
- goal / Goal and business case
- business case / Goal and business case
- spaCy
- reference link / Natural language processing
- statsmodels
- reference / ARIMA
T
- techniques
- overview / Overview of techniques
- TensorFlow
- using / TensorFlow and Keras
- reference link / Computer vision
- Term Frequency-Inverse Document Frequency (TF-IDF) / Content-based recommendations
- tuProlog
- used, for logic programming / Logic programming with Prolog and tuProlog
- Prolog, using from Java / Using Prolog from Java with tuProlog
- Twitter API / Twitter API
U
- unification / Prolog unification and resolution
W
- Wikimedia Toolforge
- reference / Z-scores with static models
Y
- You Only Look Once (YOLO)
- used, for detecting logos / YOLO and Darknet