Index

  • A
  • activations, neural networks, 113, 114
  • AGI (Artificial General Intelligence), 11
  • agile development, 201
  • AI (artificial intelligence)
    • Amazon, 10, 290
    • applications, 8–11
    • art generation, 6, 77–88, 169–180, 290
    • engines, 10
    • Google, 10
    • image generation
      • GAN, 180
      • generative models, 180
      • neural networks, 181–185
      • probability distribution, 180
    • knowledge representation, 8–9
    • Netflix, 10
    • overview, 1–2, 7–8
    • photograph generation, 290
    • planning, 10
    • research pages
    • self‐driving cars, 9
    • smart task examples, 5–6
    • software development, 202–203
    • strategy, 10
    • video surveillance and, 10
  • Ajax, 204–205
  • algorithms
    • decision trees, 54
    • Random Forest, 56
  • Amazon. See also AWS (Amazon Web Services)
    • AI (Artificial Intelligence) and, 290
    • Alexa, 104
    • SageMaker, 266
  • analytics, 5–7
    • applications
      • descriptive, 13–14
      • diagnostic, 14–15
      • predictive, 15
      • prescriptive, 16–17
    • building, 12–13
    • decision logic
      • models, data‐driven, 18
      • physics‐based analytics, 17–18
      • rules‐based analytics, 17–18
    • development, system building, 18–21
    • user interface, 271–275
  • Analytics Vidhya, 291, 293
  • Apache Spark, 269
  • applications
    • Cloud‐native, 211
    • containerized, 209
      • DevOps and, 211
    • containers, packaging as, 233–237
    • growth in size, 205
    • Kubernetes, deployment as microservice, 238–240
    • web applications, 203
      • Ajax, 204–205
      • HTML (HyperText Markup Language), 203
      • HTML 2.0, 205
      • HTTP (HyperText Transfer Protocol), 204
      • JavaScript, 204–205
      • server‐side scripting, 204–205
  • architectures
    • CNN (Convolutional Neural Networks), 143–145
    • feed‐forward architecture, 160
      • neural networks, 115
    • microservices, containers and, 212–214
    • models, 144
    • SOA (Services Oriented Architecture), 212
  • arrays, words, 97
  • art generation, 169–180
    • calculating loss, 175–176
    • content, 174–175
    • Prizma, 170, 179
    • The Starry Night (van Gogh), 171
    • style, 174–175
  • artificial neural networks, 112
  • ASCII (American Standard Code for Information Interchange), 73
  • audio data, 104
    • FFT (Fast Fourier Transform), 105
    • pressure waves, 105
    • sequence‐to‐sequence models, 104
  • autoencoders, 188–189
  • AutoML, 251–252
  • AWS (Amazon Web Services), 200, 207
    • Elastic Beanstalk, 208
    • SageMaker, 245, 270
    • virtual machines, 200
  • Azure ML Studio, 245
  • B
  • back‐propagation, 43
    • neural networks, 117–119
  • Batch Gradient Descent, 119–120
  • batch normalization network layer, 135–136
  • Bengio, Yoshua, 293
  • Bethge, Matthias, 170, 293
  • bias, 57–61
  • Big Data, Hadoop, 208
  • binary classification, 43–44
  • blackboxes, neural networks, 115
  • boilerplate code, 225
  • Bontempi, Gianluca, 188
  • C
  • CaaS (Container‐as‐a‐Service), 199, 209, 211
    • containers, 209
    • DockerHub, 211
    • Kubernetes, 214–220
    • microservices and, 211
  • Caelen, Olivier, 188
  • calculating loss, 175–176
  • CD (continuous delivery), 245
  • CGI‐Scripts, HTML content, 204
  • cGroups (Linux), 210
  • chatbots, 91
  • Chollet, François, 292
  • CI (continuous integration), 245
  • CI/CD (Continuous Integration and Continuous Delivery), 201–202, 202–203
  • classification, 43–44
    • binary, 43–44
    • data augmentation and, 150–160
    • datasets and, 48–52
    • Ensemble method, 56
    • ground truth, 53
    • logistic regression, 53
    • Random Forest algorithm, 56
    • transfer learning and, 150–160
  • clickstreams, 73
  • cloud, terminology, 206
  • cloud computing, 205
    • application size growth, 205
    • AWS (Amazon Web Services), 207
    • CaaS (Container‐as‐a‐Service), 209
    • globalization and, 205
    • IaaS (Infrastructure‐as‐a‐Service), 207, 208
    • PaaS (Platform‐as‐a‐Service), 207, 208
    • SaaS (Software‐as‐a‐Service), 207, 209
    • virtualization, 207
  • Cloud‐native applications, 211
    • containers, 200
  • CNN (Convolutional Neural Networks), 73, 128, 131, 250
    • architecture, 143–145
  • codecs (video), 89
  • columnar data, 72
  • computer vision, 77–88
  • computer vision libraries, 77
  • containerized applications, 209
    • DevOps and, 211
  • containers
    • CaaS (Container‐as‐a‐Service), 209
    • Cloud‐native applications and, 200
    • DevOps and, 211
    • Docker, 210
    • environment, 210
    • Linux, 210
    • packaging applications as, 233–237
    • video, 89
  • convolution layers, 69
  • convolution network layer, 133–135
  • corpus, 91
  • Coursera, 5
  • Courville, Aaron, 293
  • credit card fraud detection, 188
    • autoencoders, 188–189
    • creditcard.cvs file, 188
  • cross‐validation, 127
    • K‐fold cross‐validation, 127
  • Crystal Reports, 13
  • D
  • Dal Pozzolo, Andrea, 188
  • DAQ (data acquisition system), 72
  • data
    • acquisition, 267–270
      • CSV files, 267–268
      • gold datasets, 270
      • Kafka, 268
    • analytics, building, 12–13
    • augmentation, 149–150
      • classification and, 150–160
    • cleansing, 32, 246–248
      • imputation, 247
    • collection, 246–248
    • descriptive statistics, 246–247
    • frames, 33
    • overview, 1–2
    • patterns (See patterns)
    • preparation, 246–248
  • data centers, 206
  • data‐driven models, 18
  • DataRobot, 271
  • datasets
    • autoencoder, 188–189
    • classification and, 48–52
    • creditcard.csv file, 188
    • decoding, 189
    • dimensionality reduction, 188
    • encoding vector, 189
    • gold datasets, 270
    • patterns, 189
    • PCA (Principal Component Analysis), 188
    • reconstruction error, 197–198
    • timeseries, 247
    • training datasets, 151
    • validation datasets, 151
  • decision logic analytics
    • models, data‐driven, 18
    • physics‐based analytics, 17–18
    • rules‐based analytics, 17–18
  • decision trees, 54
  • Deep Models, VGGNet, 145–149
  • DeepMind, 69
  • dependent variables, 13
  • deployment, ML model, 252–253
    • edge devices, 254–263
  • descriptive analytics, 13–14, 25, 246–247
  • desktop applications, 203
    • Windows Remote Desktop for Windows, 208
  • DevOps, containers and, 211
  • diagnostic analytics, 14–15
  • digital cameras, 74
  • dimensionality reduction, 188
  • Django, 224
  • DL (Deep Learning), 11, 61
    • CNN (Convolutional Neural Networks), 131
    • images, fashion, classifying, 136–142
    • models, 114, 131–132
      • deployment, Keras, 282–286
      • MLP (multi‐layered perceptron), 131–132
    • networks, 112
      • Adam optimizer, 125
      • batch normalization layer, 135–136
      • convolution layer, 133–135
      • Cross‐Entropy Loss function, 126
      • dropout layer, 135
      • Keras, 121–126
      • pooling layer, 135
      • PyTorch, 121–126
      • TensorFlow, 121–126
    • patterns, 131
    • unstructured data, 20
  • Docker, 210
    • containers, 233–237
    • Docker files, 233–237
    • images, pushing to repository, 238
    • microservices, building, 223–228
  • documents, 91
    • corpus, 91
    • tokens, 91
  • DQN (Deep Q Networks), 65, 68–69
  • dropout, 128
  • dropout network layer, 135
  • E
  • eager execution, 172
  • Ecker, Alexander S., 170, 293
  • edge processing, 3
    • ML model deployment on edge devices, 254–263
  • Einstein (SalesForce), 245
  • EIP (Enterprise Integration Patterns), 212
  • Ellison, David, 293
  • encoder, 188
  • end‐to‐end earning, 73
  • engineering, feature engineering, 32
  • ERP (Enterprise Resource Planning), 212
  • ESB (Enterprise Service Bus), 212
  • ExpressJS, 224
  • F
  • Facebook, 200
    • AI (artificial intelligence), research pages, 290
  • fashion image classification, 136–142
  • FBLearner flow, 245
  • feature engineering, 32, 132
  • feed‐forward architecture, 160
    • neural networks, 115
  • FFT (Fast Fourier Transform), 105
  • FitBit, motion tracking, 20
  • Flash, 224
  • Fowler, Martin, 294
  • FPGAs (field‐programmable gate arrays), 5
  • G
  • gaming data, 73
  • GAN (Generative Adversarial Networks), 6, 181–185
    • image generation, 180
  • Gatys, Leon A., 170, 293
  • GCP (Google Cloud Platform), 200
    • App Engine, 208
  • General Electric, 289
  • generative models, image generation, 180
  • Gensim, 99
  • GET call (HTTP), 225
  • GIF (Graphics Interchange Format), 77
  • GIGO (Garbage In Garbage Out), 25, 246–248
  • GitHub, 294
  • GLM (generalized linear model), 274
  • globalization, cloud computing and, 205
  • gold dataset, 270
  • Goodfellow, Ian J., 293
  • Google
    • AI (artificial intelligence), research pages, 290
    • AutoML, 245, 266
    • Colab Notebook, 180
    • Colaboratory, 200, 271
      • OpenCV, 77
    • Home, 104
    • machine learning course, 291
    • Maps, 16
    • TPU, 5
  • GPS, 3
  • GPU (graphic processing unit), 201
  • gradient descent
    • linear regression, 40–43
    • neural networks, 117–119
      • Batch Gradient Descent, 119–120
      • SGD (stochastic gradient descent), 119–120
  • granularity, 80
  • ground truth, 53
    • ML model lifecycle, 245–246
  • H
  • H20
    • AI (artificial intelligence), research pages, 290
    • ML regression model, 272–275
  • Hadoop, 208
  • hardware, software‐defined, 200
  • HDF (Hierarchical Data Format), 159
  • Hinton, Geoffrey, 43
  • HTML (HyperText Markup Language), 203
    • Ajax, 204–205
    • CGI‐Scripts, 204
    • HTML 2.0, 205
    • Java Servlets, 204
    • JavaScript, 204–205
    • PHP, 204
    • stylesheets, 226
    • templates, 226
  • HTTP (HyperText Transfer Protocol), 204
  • hyper‐parameters, 30, 36, 42, 143–145
    • AutoML, 251–252
    • ML model, 251–252
  • Hypervisor, 209–210
  • I
  • IaaS (Infrastructure‐as‐a‐Service), 207, 208
  • IBM Watson, 8
    • AI (artificial intelligence), research pages, 290
  • IDE (integrated development environments), 200
  • ILSVRC (ImageNet Large Scale Visual Recognition Challenge), 143
  • image processing libraries, 77
  • ImageNet, 143–145
  • images
    • array operations, 81
    • computer vision, 77
    • digital cameras, 74
    • Docker, pushing to repository, 238
    • fashion, classifying, 136–142
    • generating
      • GAN (generative adversarial networks), 180, 181–185
      • generative models, 180
      • neural networks, 181–185
      • probability distribution, 180
    • GIF (Graphics Interchange Format), 77
    • granularity, 80
    • image processing, 77
    • JPG/JPEG (Joint Photography Experts Group), 77
    • PNG (Portable Network Graphics), 77
    • resolution, 80
    • scanners, 74
  • Inception, 144
  • independent variables, 12–13
  • industrial IoT, 289
  • Industry 4.0, 4, 289
  • Instagram, 200
  • integers, 24
  • Intel, AI (artificial intelligence), research pages, 290
  • IoT (Internet of Things)
    • connectivity, built‐in, 3
    • edge processing, 3
    • overview, 2–3
  • J
  • Jain, Kunal, 291
  • Java
    • JAR files, 208
    • microservices, 224
    • Servlets, HTML content, 204
  • JavaScript, 204–205
  • Johnson, Reid A., 188
  • JPG/JPEG (Joint Photography Experts Group), 77
  • JSON (JavaScript Object Notation), 269
  • Jupyter, 202
  • Jupyter Notebooks, 292
  • JupyterHub, 287
  • K
  • K‐fold cross‐validation, 127
  • K‐Means, 28, 34
  • Kafka, 268
  • Kaggle, 26, 291
  • Karmarkar, Abhijit, 294
  • Katacoda, 293, 294
  • KDnuggets, 293
  • Keras, 121–126, 172, 292
    • data augmentation, 149–150
    • HDF (Hierarchical Data Format), 159
    • NLP mode, 228–232
  • KNN (K‐Nearest Neighbors), 53–54
  • knowledge representation, 8–9
  • KPIs (key performance indicators), 269
  • Kubeflow, 244, 287
  • Kubernetes, 214
    • apps, deploying as microservices, 238–240
    • extensions, 244
    • microservices, building, 223–228
    • Minikube, 214
    • ML model lifecycle, 244
    • nodes, 215
    • plug‐ins, 244
    • pods, 216
    • VMs (virtual machines), 214
  • L
  • Lecun, Yann, 74
  • lemmatization, 94
  • Lewis, James, 294
  • Li, Fei Fei, 143
  • linear regression, 37–40
    • gradient descent, 40–43
  • Linux
  • logging, 286–287
  • logistic regression, 53
  • LSTM (Long Short‐Term Memory), 160, 250
  • M
  • Machine Learning Group, 188
  • MAE (mean absolute error), 119
  • Marr, Bernard, 289
  • MDM (Master Data Management), 271
  • Mesosphere, 294
  • metrics
    • precision and, 49
    • recall and, 49–50
  • microservices, 199
    • app deployment on Kubernetes, 238–240
    • architecture, containers and, 212–214
    • boilerplate code, 225
    • CaaS and, 211–212
    • Docker and, 223–228
    • ExpressJS, 224
    • HTML
      • stylesheets, 226
      • templates, 226
    • Java, 224
    • Kubernates and, 223–228
    • NodeJS, 224
    • Python, 224–228
  • Microsoft Azure, 200
  • Microsoft Azure Studio, 266
  • Minikube, 214–215
  • Mirza, Mehdi, 293
  • ML (machine learning), 10
    • community, 26–27
    • courses, 290–292
    • data acquisition, 267–270
    • data cleansing, 270–271
    • datasets, gold datasets, 270
    • deployment, automated, 278–279
    • development, lifecycle, 243–263
    • FitBit motion tracking, 20
    • hyper‐parameter tuning, 278–279
    • JSON (JavaScript Object Notation), 269
    • metrics
      • precision, 49
      • recall, 49–50
    • model development, 275–277
    • neural networks, 42, 43
    • platform (See platforms)
    • reinforcement, 31
    • supervised
      • classification, 30–31, 43–48, 52–56
      • Cost function, 30
      • Error function, 30
      • gradient descent, 30
      • hyper‐parameters, 30
      • learning rate, 30
      • model training, 30
      • models, 29
      • neural networks, 29
      • positives, 29
      • probability, 31
      • regression, 30
      • weights, 29
    • Talend, 270–271
    • Tamr, 270–271
    • training at scale, 277
    • unsupervised
      • anomaly detection, 29
      • clustering, 27–28
      • dimensionality reduction, 28
      • problems, 33–36
  • ML model
    • AutoML, 251–252
    • AWS SageMaker, 245
    • Azure ML Studio, 245
    • building, 248–251
    • CNN (Convolutional Neural Network), 250
    • data
      • cleansing, 246–248
      • collection, 246–248
      • feature engineering, 247–248
      • preparation, 246–248
      • timeseries, 247
    • deployment, 252–253
      • automated, 279–286
      • edge devices, 254–263
      • NVIDIA, 254–255
    • development, 275–277
    • Einstein, 245
    • FBLearner flow, 245
    • feedback, 253–254
    • Google AutoML, 245
    • ground truth, 245–246
    • hyper‐parameters, 251–252
      • tuning, 277–279
    • lifecycle, 244–263
    • LSTM (Long Short‐Term Memory), 250
    • pipeline, 244
    • problem definition, 245–246
    • RNN (recurrent neural network), 250
    • TensorFlow‐Serving, 253
    • training, 248–251
    • updates, 253–254
    • validation, 251–252
  • MLP (multi‐layered perceptron), 120, 131–132
  • model‐based RL, 63–64
  • model‐free RL, 63–64
  • models, 23
    • architectures, 144
    • audio data sequence‐to‐sequence models, 144
    • data‐driven, 18
    • Deep Models, VGGNet, 145–149
    • DL (Deep Learning), 114, 131–132
      • deployment, Keras, 282–286
      • MLP (multi‐layered perceptron), 131–132
    • edge processing, 254–263
    • generative models, image generation, 180
    • GLM (generalized linear model), 274
    • training, 25
  • monitoring, 286–287
  • Moore's Law of electronics, 4–5
  • MSE (mean squared error), 117–118, 119
  • MVC (Model‐View‐Controller), 212
  • N
  • NER (Named Entity Recognition), 95–97
  • networks
    • batch normalization layer, 135–136
    • convolution layer, 133–135
    • dropout layer, 135
    • pooling layer, 135
  • neural networks, 42, 43, 112–117., See also CNN (Convolutional Neural Networks)
    • activations, 113, 114
    • artificial, 112
    • back‐propagation, 117–119
    • bias neurons, 116, 117
    • blackboxes, 115
    • CNN (Convolutional Neural Networks), 250
    • computational graph, 114
    • dataflow, 114
    • Deep Learning framework
      • Adam optimizer, 125
      • Cross‐Entropy Loss function, 126
      • Keras, 121–126
      • PyTorch, 121–126
      • TensorFlow, 121–126
    • dense layer, 115
    • feature engineering, 132
    • feed‐forward architecture, 115
    • fully‐connected layer, 115
    • gradient descent, 117–119
      • Batch Gradient Descent, 119–120
      • SGD (stochastic gradient descent), 119–120
    • human brain comparison, 112–113
    • image generation
      • discriminator, 181–185
      • generator, 181–185
    • MLP (multi‐layered perceptron), 120
    • RNN (recurrent neural network), 160–166, 250
    • shallow, 114
    • weight values, 115–116, 117
  • neural style transfer, 169–180
  • Ng, Andrew, 5, 289, 290, 292
  • NLP (Natural Language Processing), 10–11, 91–97, 228, 292
    • algorithms, 90–91
    • data cleansing, 92–94
    • H5 binary files, 228
    • Keras, 228–232
    • lemmatization, 94
    • NLU (Natural Language Understanding), 91
    • stemming, 94
  • NLTK (Natural Language Tool Kit), 91
  • NLU (Natural Language Understanding), 91
  • NodeJS, 224
  • nodes, Kubernetes, 215
  • NumPy (Numerical Python), 121–126
  • NVIDIA, 254–255
    • AI (artificial intelligence), research pages, 290
    • GPU, 5
  • O
  • one‐hot encoding, 97–99
  • OpenCV, 77
    • images
      • array operations, 81
      • granularity, 80
      • loading as array, 77
      • resolution, 80
    • tutorials, 291
    • video, 89–90
  • OTA (over‐the‐air) updates, 200
  • overfitting, 57–61, 126–128
  • Ozair, Sherjil, 293
  • P
  • PaaS (Platform‐as‐a‐Service), 199, 207, 208
  • Pandas data frame, 34, 49
  • parameters, hyper‐parameters, 30, 36, 42
  • patterns, 23–24
  • PCA (Principal Component Analysis), 102, 188
  • PHP (hypertext preprocessor), HTML content, 204
  • physics‐based analytics, 17–18
  • Pichai, Sundar, 11
  • platforms, 265–266
    • Amazon SageMaker, 266, 270
    • Google AutoML, 266
    • Microsoft Azure Studio, 266
  • PNG (Portable Network Graphics), 77
  • podcasts, 291
  • pods, Kubernetes, 216
  • pooling network layer, 135
  • POS (parts of speech), 95–97
  • POST call (HTTP), 225
  • Pouget‐Abadie, Jean, 293
  • predictions, VGGNet, 145–149
  • predictive analytics, 15, 25
  • prescriptive analytics, 16–17
  • pressure waves (sound), 105
  • Prizma, 170, 179
  • probability distribution, image generation, 180
  • problem solving, 31–32
    • ML model lifecycle, 245–246
  • processing growth, 4–5
  • project management, scrum, 201
  • Python, 202
    • microservice building, 224–228
  • Q
  • Q‐Learning, 65, 66–68
  • QlikView, 13
  • R
  • Ramsundar, Bharath, 292
  • Random Forest algorithm, 56
  • real numbers, 24
  • reconstruction errors, 197–198
  • references
    • AI (artificial intelligence), 289–290
      • models as microservices, 294
      • modern software, 293
    • big data, 289–290
    • Deep Learning
      • advanced, 292–293
      • Keras, 292
      • projects, 293
    • machine learning, 290–291
      • development lifecycle, 294
      • platform, 294
    • unstructured data, 291–292
  • regression, 30
    • linear, 37–40
      • gradient descent and, 40–43
    • logistic, 53
  • regularization, 128
  • reinforcement learning, 31
  • Renelle, Tyler, 291
  • repositories, Docker images, 238
  • ResNet, 144
  • resolution, 80
  • resources, ML community, 26
  • RFID tags, 3
  • RL (Reinforcement Learning), 62
    • DQN (Deep Q Networks), 65, 68–69
    • model free, 65–66
      • exploitation, 65–66
      • exploration, 66
      • greedy policy, 66
      • random policy, 66
    • model‐based, 63–64
    • Q‐Learning, 65, 66–68
    • SARSA, 65
  • RNN (Recurrent Neural Networks), 73, 91, 160–166, 250
  • Rosebrock, Adrian, 291
  • rules‐based analytics, 17–18
  • S
  • SaaS (Software‐as‐a‐Service), 199, 207, 209
  • Salesforce Einstein, AI (artificial intelligence) research pages, 290
  • SARSA (state‐action‐reward‐state‐action), 65
  • scanners, 74
  • Scikit‐Learn, 292
  • scrum, 201
  • SDLC (software development lifecycle), 245
  • self‐driving cars, 9
  • sequence of points, 25
  • sequence‐to‐sequence models, 104
  • server‐side scripting, web applications, 204–205
  • SGD (stochastic gradient descent), 119–120, 144
  • shallow networks, 114
  • Sisense, 13
  • SOA (Services Oriented Architecture), 199, 212
  • social media, 200
  • software
    • development, AI with modern software, 202–203
    • modern, 200–202
    • sprints, 201
  • software‐defined hardware, 200
  • sound. See audio data
  • sprints, 201
  • SQL (Structured Query Language), 267–268
  • structured data, 71–74, 112
    • clickstreams, 73
    • columnar data, 72
    • credit card fraud detection, 188–198
    • datasets
      • autoencoders, 188–189
      • decoding, 189
      • dimensionality reduction, 188
      • encoding vector, 189
      • patterns, 189
      • PCA, 188
      • reconstruction error, 197–198
    • gaming data, 73
    • tabular data, 72
    • timeseries data, 72
    • weblogs, 73
  • stylesheets (HTML), 226
  • supervised ML
    • classification, 30–31, 43–44
      • binary, 43–44
    • Cost function, 30
    • Error function, 30
    • gradient descent, 30
    • hyper‐parameters, 30
    • learning rate, 30
    • model training, 30
    • models, 29
    • neural networks, 29
    • positives, 29
    • probability, 31
    • regression, 30
      • linear, 37–43
    • weights, 29
  • T
  • Tableau, 13
  • tabular data, 72
  • Talend, 270–271
  • Tamr, 270–271
  • templates (HTML), 226
  • TensorFlow, 172, 292, 293
  • TensorFlow‐Serving, 253
  • textual data, 90–91
    • chatbots, 91
    • documents, 91
      • corpus, 91
    • NER (Named Entity Recognition), 95–97
    • NLP (Natural Language Processing), 91–97
    • POS (parts of speech), 95–97
    • RNN (Recurrent Neural Networks), 91
    • TF‐IDF (term frequency‐inverse document frequency), 99
    • word embeddings, 97–103
    • word frequencies, 99
    • Word2Vec model, 102
  • TF‐IDF (term frequency‐inverse document frequency), 99
  • TF‐Job, 287
  • TF‐Serving (TensorFlow‐Serving), 280, 287
  • The Starry Night (van Gogh), 171
  • timeseries data, 72
  • tokens, 91
  • topic modeling, 99
  • TPU (Tensor Processing Unit), 255–263
  • training, models, 25
  • training datasets, 151
  • transfer learning, 149–150
    • classification and, 150–160
  • Twitter, 200
  • U
  • ULB (Université Libre de Bruxelles), 188
  • underfitting, 57–61, 126–128
  • unstructured data, 71–74, 111–112
    • end‐to‐end earning, 73
    • features, extracting, 73
    • tweets, 73
  • Unsupervised Learning, 270
  • unsupervised ML
    • anomaly detection, 29
    • clustering, 27–28
      • DBSCAN, 28
      • Hierarchical Clustering, 28
      • K‐Means, 28
    • dimensionality reduction, 28
      • PCA (principal component analysis), 28
    • problems, 33–36
  • user interface, analytics, 271–275
  • V
  • validation, ML model, 251–252
  • validation datasets, 151
  • variance, 57–61
  • Vasudevan, Gautam, 294
  • vectors
    • one‐hot encoding, 97–99
    • words, 97
  • VGG (Visual Geometry Group), 145–149
  • VGGNet, 145–149
  • video
    • codecs, 89
    • containers, 89
    • OpenCV, 89–90
    • surveillance, 10
  • virtualization, 207
  • VMs (virtual machines), 200
    • Hypervisor, 209–210
    • JAR files, 208
    • Kubernetes, 214
  • W
  • Warde‐Farley, David, 293
  • waterfall model, 201
  • Waymo self‐driving car, 9
  • web applications, 203
    • Ajax, 204–205
    • HTML (HyperText Markup Language), 203
      • JavaScript, 204–205
    • HTML 2.0, 205
    • HTTP (HyperText Transfer Protocol), 204
    • server‐side scripting, 204–205
  • web browsers, applications, 203
  • weblogs, 73
  • WhatsApp, 200
  • Windows Remote Desktop for Windows as a desktop, 208
  • word embeddings
    • arrays, 97
    • TF‐IDF (term frequency‐inverse document frequency), 99
    • topic modeling, 99
    • vectors, 97
    • word frequencies, 99
    • Word2Vec model, 102
  • word frequencies, 99
  • Word2Vec model, 102
  • X
  • Xs (independent variables), 12–13
  • Xu, Bing, 293
  • Y
  • YAML files, 239–240
  • Ys (dependent variables), 13
  • Yuan, Raymond, 172, 293
  • Z
  • Zadeh, Reza, 292
  • zettabytes, 5
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset