Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow 2
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Birmingham ; Mumbai
Packt
December 2019
|
| Edition: | Third edition |
| Series: | Expert insight
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Auf dem Cover: "includes TensorFlow 2, GANs, and reinforcement learning" |
| Physical Description: | xxi, 741 Seiten Illustrationen, Diagramme |
| ISBN: | 9781789955750 |
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| adam_text | Table of Contents Preface_____________________________________________________xiii Chapter 1: Giving Computers the Ability to Learn from Data________ 1 Building intelligent machines to transform data into knowledge 1 The three different types of machine learning 2 Making predictions about the future with supervised learning 3 Classification for predicting class labels Regression for predicting continuous outcomes Solving interactive problems with reinforcement learning Discovering hidden structures with unsupervised learning Finding subgroups with clustering Dimensionality reduction for data compression Introduction to the basic terminology and notations Notation and conventions used in this book Machine learning terminology A roadmap for building machine learning systems Preprocessing - getting data into shape Training and selecting a predictive model Evaluating models and predicting unseen data instances Using Python for machine learning Installing Python and packages from the Python Package Index Using the Anaconda Python distribution and package manager Packages for scientific computing, data science, and machine learning Summary [i] 3 4 6 7 7 8 8 9 11 11 12 13 14 14 14 15 16 16
Table of Contents___________________ ____________ ___________________________________ ______________________ —---------------------------------------------------------------------- Chapter 2: Training Simple Machine Learning Algorithms for Classification___________________ ___________ :------------------- ;— Artificial neurons - a brief glimpse into the early history of machine learning The formal definition of an artificial neuron The perceptron learning rule Implementing a perceptron learning algorithm in Python An object-oriented perceptron API Training a perceptron model on the Iris dataset Adaptive linear neurons and the convergence of learning Minimizing cost functions with gradient descent Implementing Adaline in Python Improving gradient descent through feature scaling Large-scale machine learning and stochastic gradient descent Summary Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn_____________________________________________ Choosing a classification algorithm First steps with scikit-learn - training a perceptron Modeling class probabilities via logistic regression Logistic regression and conditional probabilities Learning the weights of the logistic cost function Converting an Adaline implementation into an algorithm for logistic regression Training a logistic regression model with scikit-learn Tackling overfitting via regularization Maximum margin classification with support vector machines Maximum margin intuition Dealing with a nonlinearly separable case using slack variables Alternative implementations in scikit-learn Solving nonlinear
problems using a kernel SVM Kernel methods for linearly inseparable data Using the kernel trick to find separating hyperplanes in a high-dimensional space Decision tree learning Maximizing IG - getting the most bang for your buck Building a decision tree Combining multiple decision trees via random forests К-nearest neighbors - a lazy learning algorithm Summary 19 20 21 23 26 26 30 36 37 40 44 46 51 53 53 54 60 60 65 67 72 75 79 79 81 83 84 84 86 90 91 96 100 103 108
Table of Contents Chapter 4: Building Good Training Datasets Data Preprocessing______ ____________________________________ Dealing with missing data Identifying missing values in tabular data Eliminating training examples or features with missing values Imputing missing values Understanding the scikit-learn estimator API Handling categorical data Categorical data encoding with pandas Mapping ordinal features Encoding class labels Performing one-hot encoding on nominal features Partitioning a dataset into separate training and test datasets Bringing features onto the same scale Selecting meaningful features L1 and L2 regularization as penalties against model complexity A geometric interpretation of L2 regularization Sparse solutions with L1 regularization Sequential feature selection algorithms Assessing feature importance with random forests Summary 109 109 110 111 112 113 115 116 116 117 118 121 124 127 128 128 131 135 141 143 Chapter 5: Compressing Data via Dimensionality Reduction______ 145 Unsupervised dimensionality reduction via principal component analysis 145 The main steps behind principal component analysis 146 Extracting the principal components step by step 148 Total and explained variance 151 Feature transformation 152 Principal component analysis in scikit-learn 155 Supervised data compression via linear discriminant analysis 159 Principal component analysis versus linear discriminant analysis 159 The inner workings of linear discriminant analysis 160 Computing the scatter matrices 161 Selecting linear discriminants for the new feature subspace 164 Projecting
examples onto the new feature space 167 LDA via scikit-learn 168 Using kernel principal component analysis for nonlinear mappings 169 Kernel functions and the kernel trick 170 Implementing a kernel principal component analysis in Python 175 Example 1 - separating half-moon shapes 177 Example 2 - separating concentric circles ---------------------------------------------------- [ІИ]-----------------------------------------------------
Table of Contents Projecting new data points Kernel principal component analysis in scikit-learn Summary Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Loading the Breast Cancer Wisconsin dataset Combining transformers and estimators in a pipeline Using к-fold cross-validation to assess model performance The holdout method К-fold cross-validation Debugging algorithms with learning and validation curves Diagnosing bias and variance problems with learning curves Addressing over- and underfitting with validation curves Fine-tuning machine learning models via grid search Tuning hyperparameters via grid search Algorithm selection with nested cross-validation Looking at different performance evaluation metrics Reading a confusion matrix Optimizing the precision and recall of a classification model Plotting a receiver operating characteristic Scoring metrics for multiclass classification Dealing with class imbalance Summary Chapter 7: Combining Different Models for Ensemble Learning Learning with ensembles Combining classifiers via majority vote Implementing a simple majority vote classifier Using the majority voting principle to make predictions Evaluating and tuning the ensemble classifier Bagging — building an ensemble of classifiers from bootstrap samples Bagging in a nutshell Applying bagging to classify examples in the Wine dataset Leveraging weak learners via adaptive boosting How boosting works Applying AdaBoost using scikit-learn Summary Chapter 8: Applying Machine Learning to Sentiment Analysis
Preparing the IMDb movie review data for text processing ----- [iv] 183 187 188 191 191 192 193 195 196 197 201 201 205 207 207 209 211 211 213 216 219 220 222 223 223 227 228 234 237 243 244 245 249 250 254 257 259 259
Table of Contents Obtaining the movie review dataset Preprocessing the movie dataset into a more convenient format Introducing the bag-of-words model Transforming words into feature vectors Assessing word relevancy via term frequency-inverse document frequency Cleaning text data Processing documents into tokens Training a logistic regression model for document classification Working with bigger data - online algorithms and out-of-core learning Topic modeling with Latent Dirichlet Allocation Decomposing text documents with LDA LDA with scikit-learn Summary Chapter 9: Embedding a Machine Learning Model into a Web Application____________________________________________ Serializing fitted scikit-learn estimators Setting up an SQLite database for data storage Developing a web application with Flask Our first Flask web application Form validation and rendering Setting up the directory structure Implementing a macro using the Jinja2 templating engine Adding style via CSS Creating the result page Turning the movie review classifier into a web application Files and folders - looking at the directory tree Implementing the main application as app.py Setting up the review form Creating a results page template Deploying the web application to a public server Creating a PythonAnywhere account Uploading the movie classifier application Updating the movie classifier Summary Chapter 10: Predicting Continuous Target Variables with Regression Analysis____________ Introducing linear regression Simple linear regression Multiple linear regression 260 260 262 263 265 267 269 272 274 278 279 279
283 285 285 289 291 292 294 295 296 296 298 300 301 302 305 306 309 309 310 311 314 315 315 316 317
Table of Contents Exolorinq the Housing dataset Loading the Housing dataset into a data ^me •ssssasss:s--s¡r· Estimating the coefficient of a regression model wa sciktt-le Fittinq a robust regression model using RANSAC Evaluating the performance of linear regress.™ models Using regularized methods for regression Turning a linear regression model into a curve polynomial regression Adding polynomial terms using scikit-learn Modeling nonlinear relationships in the Housing dataset Dealing with nonlinear relationships using random forests Decision tree regression Random forest regression Summary , Chapter 11: Working with Unlabeled Data - Clustering Analysis Grouping objects by similarity using k-means К-means clustering using scikit-learn A smarter way of placing the initial cluster centroids using k-means++ Hard versus soft clustering Using the elbow method to find the optimal number of clusters Quantifying the quality of clustering via silhouette plots Organizing clusters as a hierarchical tree Grouping clusters in bottom-up fashion Performing hierarchical clustering on a distance matrix Attaching dendrograms to a heat map Applying agglomerative clustering via scikit-learn Locating regions of high density via DBSCAN Summary Chapter 12: Implementing a Multilayer Artificial Neural Network from Scratch________________________________________________ Modeling complex functions with artificial neural networks Single-layer neural network recap Introducing the multilayer neural network architecture Activating a neural network via forward propagation Classifying handwritten digits
Obtaining and preparing the MNIST dataset [vi] 318 318 320 322 325 325 330 332 334 337 339 340 342 345 346 348 350 353 353 354 358 359 361 363 367 368 369 373 375 376 382 383 383 385 387 391 393 394
Table of Contents Implementing a multilayer perceptron Training an artificial neural network Computing the logistic cost function Developing your understanding of backpropagation Training neural networks via backpropagation About the convergence in neural networks A few last words about the neural network implementation Summary Chapter 13: Parallelizing Neural Network Training with TensorFlow TensorFlow and training performance Performance challenges What is TensorFlow? How we will learn TensorFlow First steps with TensorFlow Installing TensorFlow Creating tensors in TensorFlow Manipulating the data type and shape of a tensor Applying mathematical operations to tensors Split, stack, and concatenate tensors Building input pipelines using tf.data - the TensorFlow Dataset API Creating a TensorFlow Dataset from existing tensors Combining two tensors into a joint dataset Shuffle, batch, and repeat Creating a dataset from files on your local storage disk Fetching available datasets from the tensorflow_datasets library Building an NN model in TensorFlow The TensorFlow Keras API (tf.keras) Building a linear regression model Model training via the ,compile() and ,fit() methods Building a multilayer perceptron for classifying flowers in the Iris dataset Evaluating the trained model on the test dataset Saving and reloading the trained model Choosing activation functions for multilayer neural networks Logistic function recap Estimating class probabilities in multiclass classification via the softmax function Broadening the output spectrum using a hyperbolic tangent Rectified linear
unit activation Summary 400 412 412 415 417 421 422 423 425 426 426 427 429 429 429 430 431 432 434 435 436 437 439 441 445 450 451 451 456 457 461 461 462 463 465 466 468 470 ----------------------------------------------------------------Ivü]--------- --------------------------------------------------------
Table of Contents --------------------------------------------------- rhetor U: Going D8epjrrJb_eJVLeçh^lû?-°ÎIg֊nggr^^ The key features of TensorFlow . t TensorFlow v2 TensorFlow s computation graphs, migrating Understanding computation graphs Creating a graph in TensorFlow vlx Miaratinq a graph to TensorFlow v2 Loading input data into a model: TensorFlow vl.x style Loading input data into a model: TensorFlow м2 style Improving computational performance with function decorator TensorFlow Variable objects for storing and updating Computing gradients via automatic differentiation and GradientTape Computing the gradients of the loss with respect to trainable variables Computing gradients with respect to non-trainable tensors Keeping resources for multiple gradient computations Simplifying implementations of common architectures via the Keras API Solving an XOR classification problem Making model building more flexible with Keras functional API Implementing models based on Keras Model class Writing custom Keras layers TensorFlow Estimators Working with feature columns Machine learning with pre-made Estimators Using Estimators for MNIST handwritten digit classification Creating a custom Estimator from an existing Keras model Summary Chapter 15: Classifying Images with Deep Convolutional Neural Networks The building blocks of CNNs Understanding CNNs and feature hierarchies Performing discrete convolutions Discrete convolutions in one dimension Padding inputs to control the size of the output feature maps Determining the size of the convolution output Performing a discrete
convolution in 2D Subsampling layers Putting everything together - implementing a CNN Working with multiple input or color channels Regularizing an NN with dropout Loss functions for classification 471 472 473 473 474 475 476 476 477 479 483 483 485 485 486 489 494 496 497 501 501 506 510 512 515 517 518 518 520 521 523 525 526 530 532 532 536 539
Table of Contents Implementing a deep CNN using TensorFlow The multilayer CNN architecture Loading and preprocessing the data Implementing a CNN using the TensorFlow Keras API 542 542 543 544 544 545 Configuring CNN layers in Keras Constructing a CNN in Keras Gender classification from face images using a CNN Loading the CelebA dataset Image transformation and data augmentation Training a CNN gender classifier Summary Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks Introducing sequential data Modeling sequential data - order matters Representing sequences The different categories of sequence modeling RNNs for modeling sequences Understanding the RNN looping mechanism Computing activations in an RNN Hidden-recurrence versus output-recurrence The challenges of learning long-range interactions Long short-term memory cells Implementing RNNs for sequence modeling in TensorFlow Project one - predicting the sentiment of IMDb movie reviews Preparing the movie review data Embedding layers for sentence encoding Building an RNN model Building an RNN model for the sentiment analysis task Project two - character-level language modeling in TensorFlow Preprocessing the dataset Building a character-level RNN model Evaluation phase - generating new text passages Understanding language with the Transformer model Understanding the self-attention mechanism A basic version of self-attention Parameterizing the self-attention mechanism with query, key, and value weights Multi-head attention and the Transformer block Summary Chapter 17: Generative Adversarial Networks for
Synthesizing New Data____________________ Introducing generative adversarial networks [lx] 550 551 552 558 564 567 568 568 569 570 571 571 574 577 580 582 584 585 585 590 592 594 600 601 607 609 613 614 614 616 617 618 619 620
Table of Contents Starting with autoencoders Generative models for synthesizing new data Generating new samples with GANs Understanding the loss functions of the generator and discriminator networks in a GAN model Implementing a GAN from scratch Training GAN models on Google Colab Implementing the generator and the discriminator networks Defining the training dataset Training the GAN model Improving the quality of synthesized images using a convolutional and Wasserstein GAN Transposed convolution Batch normalization Implementing the generator and discriminator Dissimilarity measures between two distributions Using EM distance in practice for GANs Gradient penalty Implementing WGAN-GP to train the DCGAN model Mode collapse Other GAN applications Summary Chapter 18: Reinforcement Learning for Decision Making in Complex Environments____________________ Introduction - learning from experience Understanding reinforcement learning Defining the agent-environment interface of a reinforcement learning system The theoretical foundations of RL Markov decision processes The mathematical formulation of Markov decision processes Visualization of a Markov process Episodic versus continuing tasks 620 623 624 626 628 628 631 636 638 646 647 648 651 657 661 662 663 667 669 670 671 672 672 674 676 676 677 679 680 RL terminology: return, policy, and value function The return Policy Value function Dynamic programming using the Bellman equation Reinforcement learning algorithms Dynamic programming Policy evaluation - predicting the value function with dynamic programming Improving the policy
using the estimated value function [x] 680 680 682 683 685 686 686 687 688
Table of Contents Policy iteration Value iteration ggg ggg Reinforcement learning with Monte Carlo State-value function estimation using MC Action-value function estimation using MC Finding an optimal policy using MC control Policy improvement ֊ computing the greedy policy from the action-value function 689 690 690 691 691 Temporal difference learning 691 TD prediction On-policy TD control (SARSA) Off-policy TD control (Q-learning) g92 693 694 Implementing our first RL algorithm Introducing the OpenAI Gym toolkit Working with the existing environments in OpenAI Gym A grid world example Implementing the grid world environment in OpenAI Gym Solving the grid world problem with Q-learning Implementing the Q-leaming algorithm 694 695 695 697 698 705 705 A glance at deep Q-learning 709 Training a DQN model according to the Q-learning algorithm Implementing a deep Q-learning algorithm 710 712 Chapter and book summary 717 Other Books You May Enjoy____________________________________ 721 Index ______________________ [xi] 725
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| any_adam_object | 1 |
| author | Raschka, Sebastian Mirjalili, Vahid |
| author_GND | (DE-588)1080537872 (DE-588)1147615993 |
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| discipline | Informatik |
| edition | Third edition |
| format | Book |
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| spellingShingle | Raschka, Sebastian Mirjalili, Vahid Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python 3.4 (DE-588)1053433689 gnd Python 3.5 (DE-588)1113598565 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Big Data (DE-588)4802620-7 gnd |
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| title | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 |
| title_alt | Tensor flow |
| title_auth | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 |
| title_exact_search | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 |
| title_full | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 Sebastian Raschka, Vahid Mirjalili |
| title_fullStr | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 Sebastian Raschka, Vahid Mirjalili |
| title_full_unstemmed | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 Sebastian Raschka, Vahid Mirjalili |
| title_short | Python machine learning |
| title_sort | python machine learning machine learning and deep learning with python scikit learn and tensorflow 2 |
| title_sub | machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python 3.4 (DE-588)1053433689 gnd Python 3.5 (DE-588)1113598565 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Python Programmiersprache Datenanalyse Python 3.4 Python 3.5 Maschinelles Lernen Big Data |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031714872&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT raschkasebastian pythonmachinelearningmachinelearninganddeeplearningwithpythonscikitlearnandtensorflow2 AT mirjalilivahid pythonmachinelearningmachinelearninganddeeplearningwithpythonscikitlearnandtensorflow2 AT raschkasebastian tensorflow AT mirjalilivahid tensorflow |
Indexes
Sonderstandort Fakultät
| Call Number: |
2000 ST 250 P99 R223(3) |
|---|---|
| Copy 1 | nicht ausleihbar Checked out – Due: 31.12.2099 Recall This |
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 ST 250 P99 R223(3) |
|---|---|
| Copy 1 | ausleihbar Checked out – Due: 15.10.2025 Recall This |