Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow
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| Sprache: | English |
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Packt
September 2017
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| Ausgabe: | Second edition |
| Schriftenreihe: | Expert insight
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| Online-Zugang: | DE-188 DE-863 DE-862 DE-29 Inhaltsverzeichnis |
| Beschreibung: | Auf dem Einband: Fully revised and updated |
| Beschreibung: | 1 Online-Ressource (xviii, 595 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781787126022 |
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Titel: Python machine learning
Autor: Raschka, Sebastian
Jahr: 2017
Table of Contents
Preface xi
Chapter 1: Giving Computers the Ability to Learn from Data 1
Building intelligent machines to transform data into knowledge 2
The three different types of machine learning 2
Making predictions about the future with supervised learning 3
Classification for predicting class labels 3
Regression for predicting continuous outcomes 5
Solving interactive problems with reinforcement learning 6
Discovering hidden structures with unsupervised learning 7
Finding subgroups with clustering 7
Dimensionality reduction for data compression 8
Introduction to the basic terminology and notations 8
A roadmap for building machine learning systems 11
Preprocessing - getting data into shape 12
Training and selecting a predictive model 12
Evaluating models and predicting unseen data instances 13
Using Python for machine learning 13
Installing Python and packages from the Python Package Index 14
Using the Anaconda Python distribution and package manager 14
Packages for scientific computing, data science, and machine learning 15
Summary 15
Chapter 2: Training Simple Machine Learning Algorithms
for Classification 17
Artificial neurons - a brief glimpse into the early history of
machine learning 18
The formal definition of an artificial neuron 19
The perceptron learning rule 21
Table of Contents
Implementing a perceptron learning algorithm in Python 24
An object-oriented perceptron API 24
Training a perceptron model on the Iris dataset 28
Adaptive linear neurons and the convergence of learning 34
Minimizing cost functions with gradient descent 35
Implementing Adaline in Python 38
Improving gradient descent through feature scaling 42
Large-scale machine learning and stochastic gradient descent 44
Summary 50
Chapter 3: A Tour of Machine Learning Classifiers
Using scikit-learn 51
Choosing a classification algorithm 52
First steps with scikit-learn - training a perceptron 52
Modeling class probabilities via logistic regression 59
Logistic regression intuition and conditional probabilities 59
Learning the weights of the logistic cost function 63
Converting an Adaline implementation into an algorithm for
logistic regression 66
Training a logistic regression model with scikit-learn 71
Tackling overfitting via regularization 73
Maximum margin classification with support vector machines 76
Maximum margin intuition 77
Dealing with a nonlinearly separable case using slack variables 79
Alternative implementations in scikit-learn 81
Solving nonlinear problems using a kernel SVM 82
Kernel methods for linearly inseparable data 82
Using the kernel trick to find separating hyperplanes in
high-dimensional space 84
Decision tree learning 88
Maximizing information gain - getting the most bang for your buck 90
Building a decision tree 95
Combining multiple decision trees via random forests 98
K-nearest neighbors - a lazy learning algorithm 101
Summary 105
Chapter 4: Building Good Training Sets - Data Preprocessing 107
Dealing with missing data 107
Identifying missing values in tabular data 108
Eliminating samples or features with missing values 109
Imputing missing values 110
Understanding the scikit-learn estimator API 111
Table of Contents
Handling categorical data 112
Nominal and ordinal features 113
Creating an example dataset 113
Mapping ordinal features 113
Encoding class labels 114
Performing one-hot encoding on nominal features 116
Partitioning a dataset into separate training and test sets 118
Bringing features onto the same scale 120
Selecting meaningful features 123
L1 and L2 regularization as penalties against model complexity 124
A geometric interpretation of L2 regularization 124
Sparse solutions with L1 regularization 126
Sequential feature selection algorithms 130
Assessing feature importance with random forests 136
Summary 139
Chapter 5: Compressing Data via Dimensionality Reduction 141
Unsupervised dimensionality reduction via principal
component analysis 142
The main steps behind principal component analysis 142
Extracting the principal components step by step 144
Total and explained variance 147
Feature transformation 148
Principal component analysis in scikit-learn 151
Supervised data compression via linear discriminant analysis 155
Principal component analysis versus linear discriminant analysis 155
The inner workings of linear discriminant analysis 156
Computing the scatter matrices 157
Selecting linear discriminants for the new feature subspace 160
Projecting samples onto the new feature space 162
LDA via scikit-learn 163
Using kernel principal component analysis for nonlinear mappings 165
Kernel functions and the kernel trick 166
Implementing a kernel principal component analysis in Python 172
Example 1 - separating half-moon shapes 173
Example 2 - separating concentric circles 176
Projecting new data points 179
Kernel principal component analysis in scikit-learn 183
Summary 184
Table of Contents
Chapter 6: Learning Best Practices for Model Evaluation and
Hyperparameter Tuning 185
Streamlining workflows with pipelines 185
Loading the Breast Cancer Wisconsin dataset 186
Combining transformers and estimators in a pipeline 187
Using k-fold cross-validation to assess model performance 189
The holdout method 190
K-fold cross-validation 191
Debugging algorithms with learning and validation curves 195
Diagnosing bias and variance problems with learning curves 196
Addressing over-and underfitting with validation curves 199
Fine-tuning machine learning models via grid search 201
Tuning hyperparameters via grid search 201
Algorithm selection with nested cross-validation 203
Looking at different performance evaluation metrics 205
Reading a confusion matrix 206
Optimizing the precision and recall of a classification model 207
Plotting a receiver operating characteristic 210
Scoring metrics for multiclass classification 213
Dealing with class imbalance 214
Summary 216
Chapter 7: Combining Different Models for Ensemble Learning 219
Learning with ensembles 219
Combining classifiers via majority vote 224
Implementing a simple majority vote classifier 224
Using the majority voting principle to make predictions 231
Evaluating and tuning the ensemble classifier 234
Bagging - building an ensemble of classifiers from
bootstrap samples 240
Bagging in a nutshell 240
Applying bagging to classify samples in the Wine dataset 242
Leveraging weak learners via adaptive boosting 246
How boosting works 246
Applying AdaBoost using scikit-learn 251
Summary 254
Chapter 8: Applying Machine Learning to Sentiment Analysis 255
Preparing the IMDb movie review data for text processing 256
Obtaining the movie review dataset 256
Preprocessing the movie dataset into more convenient format 257
Table of Contents
Introducing the bag-of-words model
259
Transforming words into feature vectors
259
Assessing word relevancy via term frequency-inverse
document frequency
261
Cleaning text data
264
Processing documents into tokens
266
Training a logistic regression model for document classification
268
Working with bigger data - online algorithms and
out-of-core learning
270
Topic modeling with Latent Dirichlet Allocation
274
Decomposing text documents with LDA
275
LDA with scikit-learn
275
Summary
279
Chapter 9: Embedding a Machine Learning Model into a
Web Application
281
Serializing fitted scikit-learn estimators
282
Setting up an SQLite database for data storage
285
Developing a web application with Flask
287
Our first Flask web application
288
Form validation and rendering
290
Setting up the directory structure
291
Implementing a macro using the Jinja2 templating engine
292
Adding style via CSS
293
Creating the result page
294
Turning the movie review classifier into a web application
294
Files and folders - looking at the directory tree
296
Implementing the main application as app.py
298
Setting up the review form
300
Creating a results page template
302
Deploying the web application to a public server
304
Creating a PythonAnywhere account
304
Uploading the movie classifier application
305
Updating the movie classifier
306
Summary
308
Chapter 10: Predicting Continuous Target Variables
with Rearession Analysis
309
Introducing linear regression
310
Simple linear regression
310
Multiple linear regression
311
Exploring the Housing dataset
312
Loading the Housing dataset into a data frame
313
Table of Contents
Visualizing the important characteristics of a dataset 314
Looking at relationships using a correlation matrix 316
Implementing an ordinary least squares linear regression model 319
Solving regression for regression parameters with gradient descent 319
Estimating coefficient of a regression model via scikit-learn 324
Fitting a robust regression model using RANSAC 325
Evaluating the performance of linear regression models 328
Using regularized methods for regression 332
Turning a linear regression model into a curve— polynomial
regression 334
Adding polynomial terms using scikit-learn 334
Modeling nonlinear relationships in the Housing dataset 336
Dealing with nonlinear relationships using random forests 339
Decision tree regression 340
Random forest regression 342
Summary 345
Chapter 11: Working with Unlabeled Data - Clustering Analysis 347
Grouping objects by similarity using k-means 348
K-means clustering using scikit-learn 348
A smarter way of placing the initial cluster centroids using k-means++ 353
Hard versus soft clustering 354
Using the elbow method to find the optimal number of clusters 357
Quantifying the quality of clustering via silhouette plots 358
Organizing clusters as a hierarchical tree 363
Grouping clusters in bottom-up fashion 364
Performing hierarchical clustering on a distance matrix 365
Attaching dendrograms to a heat map 369
Applying agglomerative clustering via scikit-learn 371
Locating regions of high density via DBSCAN 372
Summary 378
Chapter 12: Implementing a Multilayer Artificial Neural
Network from Scratch 379
Modeling complex functions with artificial neural networks 380
Single-layer neural network recap 382
Introducing the multilayer neural network architecture 384
Activating a neural network via forward propagation 387
Classifying handwritten digits 389
Obtaining the MNIST dataset 390
Implementing a multilayer perceptron 396
[vi]
Table of Contents
Training an artificial neural network 407
Computing the logistic cost function 408
Developing your intuition for backpropagation 411
Training neural networks via backpropagation 412
About the convergence in neural networks 417
A few last words about the neural network implementation 418
Summary 419
Chapter 13: Parallelizing Neural Network Training
with TensorFlow 421
TensorFlow and training performance 421
What is TensorFlow? 423
How we will learn TensorFlow 424
First steps with TensorFlow 424
Working with array structures 427
Developing a simple model with the low-level TensorFlow API 428
Training neural networks efficiently with high-level TensorFlow APIs 433
Building multilayer neural networks using TensorFlow's Layers API 434
Developing a multilayer neural network with Keras 438
Choosing activation functions for multilayer networks 443
Logistic function recap 444
Estimating class probabilities in multiclass classification via the
softmax function 446
Broadening the output spectrum using a hyperbolic tangent 447
Rectified linear unit activation 449
Summary 451
Chapter 14: Going Deeper - The Mechanics of TensorFlow 453
Key features of TensorFlow 454
TensorFlow ranks and tensors 454
How to get the rank and shape of a tensor 455
Understanding TensorFlow's computation graphs 456
Placeholders in TensorFlow 459
Defining placeholders 459
Feeding placeholders with data 460
Defining placeholders for data arrays with varying batchsizes 461
Variables in TensorFlow 462
Defining variables 463
Initializing variables 465
Variable scope 466
Reusing variables 468
[vii]
Table of Contents
Building a regression model 471
Executing objects in a TensorFlow graph using their names 475
Saving and restoring a model in TensorFlow 476
Transforming Tensors as multidimensional data arrays 479
Utilizing control flow mechanics in building graphs 483
Visualizing the graph with TensorBoard 487
Extending your TensorBoard experience 490
Summary 491
Chapter 15: Classifying Images with Deep Convolutional
Neural Networks ¦ 493
Building blocks of convolutional neural networks 494
Understanding CNNs and learning feature hierarchies 494
Performing discrete convolutions 496
Performing a discrete convolution in one dimension 496
The effect of zero-padding in a convolution 499
Determining the size of the convolution output 501
Performing a discrete convolution in 2D 502
Subsampling 506
Putting everything together to build a CNN 508
Working with multiple input or color channels 508
Regularizing a neural network with dropout 512
Implementing a deep convolutional neural network
using TensorFlow 514
The multilayer CNN architecture 514
Loading and preprocessing the data 516
Implementing a CNN in the TensorFlow low-level API 517
Implementing a CNN in the TensorFlow Layers API 530
Summary 536
Chapter 16: Modeling Sequential Data Using Recurrent
Neural Networks 537
Introducing sequential data 538
Modeling sequential data - order matters 538
Representing sequences 539
The different categories of sequence modeling 540
RNNs for modeling sequences 541
Understanding the structure and flow of an RNN 541
Computing activations in an RNN 543
The challenges of learning long-range interactions 546
LSTM units 548
Table of Contents
Implementing a multilayer RNN for sequence modeling in
TensorFlow 550
Project one - performing sentiment analysis of IMDb movie
reviews using multilayer RNNs 551
Preparing the data 552
Embedding 556
Building an RNN model 558
The SentimentRNN class constructor 559
The build method 560
Step 1 - defining multilayer RNN cells 562
Step 2 - defining the initial states for the RNN cells 562
Step 3 - creating the RNN using the RNN cells and their states 563
The train method 563
The predict method 565
Instantiating the SentimentRNN class 565
Training and optimizing the sentiment analysis RNN model 566
Project two - implementing an RNN for character-level
language modeling in TensorFlow 567
Preparing the data 568
Building a character-level RNN model 572
The constructor 573
The build method 574
The train method 576
The sample method 578
Creating and training the CharRNN Model 579
The CharRNN model in the sampling mode 580
Chapter and book summary 580
Index 583 |
| any_adam_object | 1 |
| author | Raschka, Sebastian Mirjalili, Vahid |
| author_GND | (DE-588)1080537872 (DE-588)1147615993 |
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| discipline | Informatik |
| edition | Second edition |
| format | Electronic eBook |
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| id | DE-604.BV044713511 |
| illustrated | Not Illustrated |
| indexdate | 2024-10-19T04:02:05Z |
| institution | BVB |
| isbn | 9781787126022 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030109994 |
| oclc_num | 1019893789 |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS DE-862 DE-BY-FWS DE-83 DE-29 DE-188 |
| owner_facet | DE-863 DE-BY-FWS DE-862 DE-BY-FWS DE-83 DE-29 DE-188 |
| physical | 1 Online-Ressource (xviii, 595 Seiten) Illustrationen, Diagramme |
| psigel | ebook ZDB-4-EBA ZDB-30-PQE ZDB-5-WPSE ZDB-4-EBA ZDB-4-EBA 2024 ZDB-30-PQE UER_PDA_PQE_Kauf |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt |
| record_format | marc |
| series2 | Expert insight |
| spellingShingle | Raschka, Sebastian Mirjalili, Vahid Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow Python 3.5 (DE-588)1113598565 gnd Big Data (DE-588)4802620-7 gnd Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python 3.4 (DE-588)1053433689 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1113598565 (DE-588)4802620-7 (DE-588)4434275-5 (DE-588)4123037-1 (DE-588)1053433689 (DE-588)4193754-5 |
| title | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow |
| title_auth | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow |
| title_exact_search | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow |
| title_full | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow Sebastian Raschka, Vahid Mirjalili |
| title_fullStr | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow Sebastian Raschka, Vahid Mirjalili |
| title_full_unstemmed | Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow 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 |
| title_sub | machine learning and deep learning with Python, scikit-learn, and TensorFlow |
| topic | Python 3.5 (DE-588)1113598565 gnd Big Data (DE-588)4802620-7 gnd Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python 3.4 (DE-588)1053433689 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Python 3.5 Big Data Python Programmiersprache Datenanalyse Python 3.4 Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030109994&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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