Artificial intelligence with Python cookbook: proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6
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| Language: | English |
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Birmingham ; Mumbai
Packt
October 2020
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| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | viii, 453 Seiten Illustrationen, Diagramme |
| ISBN: | 9781789133967 |
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| adam_text | Table of Contents Preface_____________________ ________________________ Chapter 1 : Getting Started with Artificial Intelligence in Python Technical requirements Setting up a Jupyter environment Getting ready How to do it... Installing libraries with Google Colab Self-hosting a Jupyter Notebook environment How it works... There s more... See also Getting proficient in Python for Al Getting ready How to do it... Obtaining the history of Jupyter commands and outputs Execution history Outputs Auto-reloading packages Debugging Timing code execution Displaying progress bars Compiling your code Speeding up pandas DataFrames Parallelizing your code See also Classifying in scikit-learn, Keras, and PyTorch Getting ready How to do it... Visualizing data in seaborn Modeling in scikit-learn Modeling in Keras Modeling in PyTorch How it works... Neural network training The SELU activation function Softmax activation Cross-entropy See also Modeling with Keras Getting ready How to do it... ________1 ց g 10 ю 11 11 12 15 15 16 17 18 19 19 20 20 20 21 22 23 24 26 27 29 зо зо 31 32 34 36 43 46 46 50 50 50 51 52 53 54
Table of Contents Data loading and preprocessing Model training How it works... Maximal information coefficient Data generators Permutation importance See also Chapter 2: Advanced Topics in Supervised Machine Learning Technical requirements Transforming data in scikit-learn Getting ready How to do it... Encoding ranges numerically Deriving higher-order features Combining transformations How it works... There s more... See also Predicting house prices in PyTorch Getting ready How to do it... How it works... There s more... See also Live decisioning customer values Getting ready How to do it... How it works... Active learning Hoeffding Tree Class weighting See also Battling algorithmic bias 55 63 67 67 68 69 69 71 72 72 73 74 74 77 79 81 82 83 83 83 86 93 94 96 97 97 98 101 102 102 102 103 юз Getting ready How to do it... HOW it Works... Ю4 106 114 There s more... See also 118 Forecasting C02 time series Getting ready How to do it... Analyzing time series using ARIMA and SARIMA How it works... There s more... See also 115 119 119 120 122 124 127 128
Table of Contents Chapter 3: Patterns, Outliers, and Recommendations Clustering market segments Getting ready How to do it... How it works... There s more... See also Discovering anomalies Getting ready How to do it... How it works... к-nearest neighbors Isolation forest Autoencoder See also Representing for similarity search Getting ready How to do it... Baseline ֊ string comparison functions Bag-of-characters approach Siamese neural network approach How it works... Recommending products Getting ready How to do it... How it works... Precision at к Matrix factorization The lightfm model See also 131 132 132 133 138 140 142 143 143 144 151 151 151 151 152 152 153 154 155 156 157 161 162 162 164 166 167 167 168 168 Spotting fraudster communities 169 Getting ready How to do it... Creating an adjacency matrix Community detection algorithms Evaluating the communities How it works... Graph community algorithms 169 170 170 171 172 174 174 174 175 175 175 177 Louvain algorithm Girvan-Newman algorithm Information entropy There s more... See also Chapter 4: Probabilistic Modeling Technical requirements Predicting stock prices with confidence 179 iso iso ------------------------------------------------------------ till] -----------------------------------------------------------
Table of Contents Getting ready How to do it... How it works... Featurization Platt scaling Isotonic regression Naive Bayes See also 180 181 185 185 186 187 187 188 Estimating customer lifetime value Getting ready How to do it... How it works... The BG/NBD model The Gamma-Gamma model See also 189 189 191 192 192 192 193 193 194 199 199 Diagnosing a disease Getting ready How to do it... How it works... Aleatoric uncertainty Negative log-likelihood Bernoulli distribution Metrics See also 200 200 200 201 201 201 202 206 Stopping credit defaults Getting ready How to do it... How it works... Epistemic uncertainty See also 206 207 Chapter 5: Heuristic Search Techniques and Logical Inference Making decisions based on knowledge Getting ready How to do it... Logical reasoning Knowledge embedding How it works... Logical reasoning Logic provers Knowledge embedding Graph embedding with Walklets See also Solving the n-queens problem Getting ready How to do it... Genetic algorithm --------------------------------------------------------- 18Ց 209 209 210 210 211 211 215 216 217 217 217 218 220 220 221 221 [iv] ---------------------------------------------------------
Table of Contents Particle swarm optimization SAT solver 225 229 How it works... Genetic algorithm Particle swarm optimization SAT solver See also 231 Finding the shortest bus route 236 231 233 234 236 Getting ready How to do it... Simulated annealing Ant colony optimization How it works... Simulated annealing Ant colony optimization See also 2Յ6 237 237 239 240 241 241 242 Simulating the spread of a disease 243 243 Getting ready HOW tO do it... How it works... There s more... See also 244 251 253 Writing a chess engine with Monte Carlo tree search Getting ready How to do it... Tree search Implementing a node Playing chess How it works... There s more... See also 255 255 257 258 260 261 262 Chapter 6: Deep Reinforcement Learning Technical requirements Optimizing a website How to do it... How İt works... See also Controlling a cartpole Getting ready How to do it... How it works... There s more... Watching our agents in the environment Using the RLlib library See also Playing blackjack --------------------------------------------------------- 254 254 254 26Յ 264 264 264 270 272 272 273 273 279 280 280 281 281 282 [v] ---------------------------------------------------------
Table of Contents Getting ready How to do it... How it works... See also 282 283 289 291 Chapter 7: Advanced Image Applications Technical requirements Recognizing clothing items Getting ready How to do it... Difference of Gaussiane Multilayer perceptron Le N été MobileNet transfer learning How it works... Difference of Gaussian LeNet5 MobileNet transfer learning See also Generating images Getting ready How to do it... How it works... See also Encoding images and style Getting ready How to do it... How it works... See also Chapter 8: Working with Moving Images Technical requirements Localizing objects Getting ready How to do it... How it works... There s more... See also Faking videos Getting ready How to do it... How it works... See also Deep fakes Detection of deep fakes Chapter 9: Deep Learning in Audio and Speech [vi] 293 293 293 295 295 296 297 299 301 302 303 303 304 305 306 306 306 313 314 314 315 315 324 326 327 328 328 328 329 332 333 334 335 335 336 338 341 341 342 343
Table of Contents Technical requirements Recognizing voice commands Getting ready How to do it... How it works... See also Synthesizing speech from text Getting ready How to do it... How it works... Deep Convolutional Networks with Guided Attention WaveGAN There s more... See also 344 344 344 345 351 351 352 352 353 355 355 356 357 358 359 359 360 364 365 Generating melodies Getting ready How to do it... How it works... See also Chapter 10: Natural Language Processing Technical requirements Classifying newsgroups Getting ready How to do it... Bag-of-words Word embeddings Custom word embeddings How it works... The CBOW algorithm TFIDF There s more... See also 367 З68 З68 З68 369 370 370 372 373 374 375 375 377 Chatting to users Getting ready How to do it... How it works... ELIZA Eywa See also Translating a text from English to German Getting ready How to do it... How it works... There s more... See also [vii] 378 379 379 383 383 384 385 З66 386 387 398 400 400
Table of Contents Writing a popular novel 401 402 403 406 407 Getting ready How to do it... How it works... See also Chapter 11: Artificial Intelligence in Production Technical requirements Visualizing model results Getting ready How to do it... Streamlit hello-world Creating our data app How it works... See also Serving a model for live decisioning Getting ready How to do it... How it works... Monitoring See also Securing a model against attack Getting ready How to do it... How it works... Differential privacy Private aggregation of teacher ensembles See also Other Books You May Enjoy Index 409 409 410 410 410 410 411 418 419 420 420 421 425 426 427 428 429 429 436 437 438 439 441 445 [ viii ]
Artificial Intelligence with Python Artificial intelligence (Al) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you ll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you ll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and Al use cases in the healthcare and insurance industries. Throughout the book, you ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on Al, you will have the skills you need to write Al and machine learning algorithms, test them, and deploy them for production. Things you will learn: • • • • Implement data preprocessing steps and optimize model hyperparameters Delve into representational learning with adversial autoencoders Use active learning, recommenders, knowledge embedding, and SAT solvers Get to grips with probabilistic modeling with
TensorFlow probability • • • • Run object detection, text-to-speech conversion, and text and music generation Apply swarm algorithms, multi-agent systems, and graph networks Go from proof of concept to production by deploying models as microservices Understand how to use modern Al in practice
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| adam_txt |
Table of Contents Preface_ _ Chapter 1 : Getting Started with Artificial Intelligence in Python Technical requirements Setting up a Jupyter environment Getting ready How to do it. Installing libraries with Google Colab Self-hosting a Jupyter Notebook environment How it works. There's more. See also Getting proficient in Python for Al Getting ready How to do it. Obtaining the history of Jupyter commands and outputs Execution history Outputs Auto-reloading packages Debugging Timing code execution Displaying progress bars Compiling your code Speeding up pandas DataFrames Parallelizing your code See also Classifying in scikit-learn, Keras, and PyTorch Getting ready How to do it. Visualizing data in seaborn Modeling in scikit-learn Modeling in Keras Modeling in PyTorch How it works. Neural network training The SELU activation function Softmax activation Cross-entropy See also Modeling with Keras Getting ready How to do it. _1 ց g 10 ю 11 11 12 15 15 16 17 18 19 19 20 20 20 21 22 23 24 26 27 29 зо зо 31 32 34 36 43 46 46 50 50 50 51 52 53 54
Table of Contents Data loading and preprocessing Model training How it works. Maximal information coefficient Data generators Permutation importance See also Chapter 2: Advanced Topics in Supervised Machine Learning Technical requirements Transforming data in scikit-learn Getting ready How to do it. Encoding ranges numerically Deriving higher-order features Combining transformations How it works. There's more. See also Predicting house prices in PyTorch Getting ready How to do it. How it works. There's more. See also Live decisioning customer values Getting ready How to do it. How it works. Active learning Hoeffding Tree Class weighting See also Battling algorithmic bias 55 63 67 67 68 69 69 71 72 72 73 74 74 77 79 81 82 83 83 83 86 93 94 96 97 97 98 101 102 102 102 103 юз Getting ready How to do it. HOW it Works. Ю4 106 114 There's more. See also 118 Forecasting C02 time series Getting ready How to do it. Analyzing time series using ARIMA and SARIMA How it works. There's more. See also 115 119 119 120 122 124 127 128
Table of Contents Chapter 3: Patterns, Outliers, and Recommendations Clustering market segments Getting ready How to do it. How it works. There's more. See also Discovering anomalies Getting ready How to do it. How it works. к-nearest neighbors Isolation forest Autoencoder See also Representing for similarity search Getting ready How to do it. Baseline ֊ string comparison functions Bag-of-characters approach Siamese neural network approach How it works. Recommending products Getting ready How to do it. How it works. Precision at к Matrix factorization The lightfm model See also 131 132 132 133 138 140 142 143 143 144 151 151 151 151 152 152 153 154 155 156 157 161 162 162 164 166 167 167 168 168 Spotting fraudster communities 169 Getting ready How to do it. Creating an adjacency matrix Community detection algorithms Evaluating the communities How it works. Graph community algorithms 169 170 170 171 172 174 174 174 175 175 175 177 Louvain algorithm Girvan-Newman algorithm Information entropy There's more. See also Chapter 4: Probabilistic Modeling Technical requirements Predicting stock prices with confidence 179 iso iso ------------------------------------------------------------ till] -----------------------------------------------------------
Table of Contents Getting ready How to do it. How it works. Featurization Platt scaling Isotonic regression Naive Bayes See also 180 181 185 185 186 187 187 188 Estimating customer lifetime value Getting ready How to do it. How it works. The BG/NBD model The Gamma-Gamma model See also 189 189 191 192 192 192 193 193 194 199 199 Diagnosing a disease Getting ready How to do it. How it works. Aleatoric uncertainty Negative log-likelihood Bernoulli distribution Metrics See also 200 200 200 201 201 201 202 206 Stopping credit defaults Getting ready How to do it. How it works. Epistemic uncertainty See also 206 207 Chapter 5: Heuristic Search Techniques and Logical Inference Making decisions based on knowledge Getting ready How to do it. Logical reasoning Knowledge embedding How it works. Logical reasoning Logic provers Knowledge embedding Graph embedding with Walklets See also Solving the n-queens problem Getting ready How to do it. Genetic algorithm --------------------------------------------------------- 18Ց 209 209 210 210 211 211 215 216 217 217 217 218 220 220 221 221 [iv] ---------------------------------------------------------
Table of Contents Particle swarm optimization SAT solver 225 229 How it works. Genetic algorithm Particle swarm optimization SAT solver See also 231 Finding the shortest bus route 236 231 233 234 236 Getting ready How to do it. Simulated annealing Ant colony optimization How it works. Simulated annealing Ant colony optimization See also 2Յ6 237 237 239 240 241 241 242 Simulating the spread of a disease 243 243 Getting ready HOW tO do it. How it works. There's more. See also 244 251 253 Writing a chess engine with Monte Carlo tree search Getting ready How to do it. Tree search Implementing a node Playing chess How it works. There's more. See also 255 255 257 258 260 261 262 Chapter 6: Deep Reinforcement Learning Technical requirements Optimizing a website How to do it. How İt works. See also Controlling a cartpole Getting ready How to do it. How it works. There's more. Watching our agents in the environment Using the RLlib library See also Playing blackjack --------------------------------------------------------- 254 254 254 26Յ 264 264 264 270 272 272 273 273 279 280 280 281 281 282 [v] ---------------------------------------------------------
Table of Contents Getting ready How to do it. How it works. See also 282 283 289 291 Chapter 7: Advanced Image Applications Technical requirements Recognizing clothing items Getting ready How to do it. Difference of Gaussiane Multilayer perceptron Le N été MobileNet transfer learning How it works. Difference of Gaussian LeNet5 MobileNet transfer learning See also Generating images Getting ready How to do it. How it works. See also Encoding images and style Getting ready How to do it. How it works. See also Chapter 8: Working with Moving Images Technical requirements Localizing objects Getting ready How to do it. How it works. There's more. See also Faking videos Getting ready How to do it. How it works. See also Deep fakes Detection of deep fakes Chapter 9: Deep Learning in Audio and Speech [vi] 293 293 293 295 295 296 297 299 301 302 303 303 304 305 306 306 306 313 314 314 315 315 324 326 327 328 328 328 329 332 333 334 335 335 336 338 341 341 342 343
Table of Contents Technical requirements Recognizing voice commands Getting ready How to do it. How it works. See also Synthesizing speech from text Getting ready How to do it. How it works. Deep Convolutional Networks with Guided Attention WaveGAN There's more. See also 344 344 344 345 351 351 352 352 353 355 355 356 357 358 359 359 360 364 365 Generating melodies Getting ready How to do it. How it works. See also Chapter 10: Natural Language Processing Technical requirements Classifying newsgroups Getting ready How to do it. Bag-of-words Word embeddings Custom word embeddings How it works. The CBOW algorithm TFIDF There's more. See also 367 З68 З68 З68 369 370 370 372 373 374 375 375 377 Chatting to users Getting ready How to do it. How it works. ELIZA Eywa See also Translating a text from English to German Getting ready How to do it. How it works. There's more. See also [vii] 378 379 379 383 383 384 385 З66 386 387 398 400 400
Table of Contents Writing a popular novel 401 402 403 406 407 Getting ready How to do it. How it works. See also Chapter 11: Artificial Intelligence in Production Technical requirements Visualizing model results Getting ready How to do it. Streamlit hello-world Creating our data app How it works. See also Serving a model for live decisioning Getting ready How to do it. How it works. Monitoring See also Securing a model against attack Getting ready How to do it. How it works. Differential privacy Private aggregation of teacher ensembles See also Other Books You May Enjoy Index 409 409 410 410 410 410 411 418 419 420 420 421 425 426 427 428 429 429 436 437 438 439 441 445 [ viii ]
Artificial Intelligence with Python Artificial intelligence (Al) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you'll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and Al use cases in the healthcare and insurance industries. Throughout the book, you'll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on Al, you will have the skills you need to write Al and machine learning algorithms, test them, and deploy them for production. Things you will learn: • • • • Implement data preprocessing steps and optimize model hyperparameters Delve into representational learning with adversial autoencoders Use active learning, recommenders, knowledge embedding, and SAT solvers Get to grips with probabilistic modeling with
TensorFlow probability • • • • Run object detection, text-to-speech conversion, and text and music generation Apply swarm algorithms, multi-agent systems, and graph networks Go from proof of concept to production by deploying models as microservices Understand how to use modern Al in practice |
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| spelling | Auffarth, Ben Verfasser aut Artificial intelligence with Python cookbook proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 Ben Auffarth Birmingham ; Mumbai Packt October 2020 viii, 453 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032609871&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032609871&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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| title | Artificial intelligence with Python cookbook proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_auth | Artificial intelligence with Python cookbook proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
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| title_full | Artificial intelligence with Python cookbook proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 Ben Auffarth |
| title_fullStr | Artificial intelligence with Python cookbook proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 Ben Auffarth |
| title_full_unstemmed | Artificial intelligence with Python cookbook proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 Ben Auffarth |
| title_short | Artificial intelligence with Python cookbook |
| title_sort | artificial intelligence with python cookbook proven recipes for applying ai algorithms and deep learning techniques using tensorflow 2 x and pytorch 1 6 |
| title_sub | proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Python Programmiersprache Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032609871&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032609871&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT auffarthben artificialintelligencewithpythoncookbookprovenrecipesforapplyingaialgorithmsanddeeplearningtechniquesusingtensorflow2xandpytorch16 |