Building machine learning systems with Python: explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Birmingham ; Mumbai
Packt
2018
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| Edition: | Third edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes index |
| Physical Description: | vi, 392 pages Illustrationen 24 cm |
| ISBN: | 9781788623223 |
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| adam_text | Table of Contents Preface ___ _______________________________________ 1 Chapter 1 : Getting Started with Python Machine Learning Machine learning and Python - a dream team What the book will teach you - and what it will not How to best read this book What to do when you are stuck Getting started Introduction to NumPy, SciPy, Matplotlib, and TensorFlow Installing Python Chewing data efficiently with NumPy and intelligently with SciPy Learning NumPy Indexing Handling nonexistent values Comparing the runtime Learning SciPy Fundamentals of machine learning Asking a question Getting answers Our first (tiny) application of machine learning Reading in the data Preprocessing and cleaning the data Choosing the right model and learning algorithm Before we build our first model Starting with a simple straight line Toward more complex models Stepping back to go forward - another look at our data Training and testing Answering our initial question Summary Chapter 2: Classifying with Real-World Examples The Iris dataset Visualization is a good first step Classifying with scikit-learn Building our first classification model Evaluation - holding out data and cross-validation How to measure and compare classifiers A more complex dataset and the nearest-neighbor classifier Learning about the seeds dataset Features and feature engineering Nearest neighbor classification Looking at the decision boundaries 7 в 9 10 12 12 13 13 14 14 17 17 18 19 20 21 21 21 22 22 25 26 26 28 31 34 36 38 յց 40 40 42 42 43 47 48 48 49 50 51
Table of Contents Which classifier to use Summary Chapter 3: Regression Predicting house prices with regression Multidimensional regression Cross-validation for regression 57 57 61 62 Penalized or regularized regression L1 and 12 penalties 63 64 Using Lasso or ElasticNet in scikit-learn Visualizing the Lasso path P-greater-than-N scenarios An example based on text documents Setting hyperparameters in a principled way Regression with TensorFlow Summary Chapter 4: Classification I - Detecting Poor Answers Sketching our roadmap Learning to classify classy answers Tuning the instance Tuning the classifier Fetching the data Slimming the data down to chewable chunks Preselecting and processing attributes Defining what a good answer is Creating our first classifier Engineering the features Training the classifier Measuring the classifier s performance Designing more features Deciding how to improve the performance Bias, variance and their trade-off Fixing high bias Fixing high variance High or low bias? 54 55 66 66 67 68 70 74 79 81 82 82 82 82 83 84 84 86 87 87 89 89 90 94 95 95 95 96 Using logistic regression 99 A bit of math with a small example Applying logistic regression to our post-classification problem Looking behind accuracy - precision and recall Slimming the classifier Ship İt! Classification using Tensorflow Summary [ii] 99 102 104 107 108 109 115
Table of Contents Chapter S: Dimensionality Reduction Sketching our roadmap Selecting features Detecting redundant features using filters Correlation Mutual information Asking the model about the features using wrappers Other feature selection methods Feature projection Principal component analysis Sketching PCA Applying PCA Limitations of PCA and how LDA can help Multidimensional scaling Autoencoders, or neural networks for dimensionality reduction Summary Chapter 6: Clustering - Finding Related Posts Measuring the relatedness of posts How not to do it How to do İt Preprocessing - similarity measured as a similar number of common words Converting raw text into a bag of words Counting words Normalizing word count vectors Removing less important words Stemming Installing and using NLTK Extending the vectorizer with N LTK s stemmer Stop words on steroids Our achievements and goals Clustering К-means Getting test data to evaluate our ideas Clustering posts Solving our initial challenge Another look at noise Tweaking the parameters Summary Chapter 7: Recommendations Rating predictions and recommendations Splitting into training and testing Normalizing the training data A neighborhood approach to recommendations 117 ns 118 118 119 122 127 130 130 131 131 132 133 135 1Յ8 144 145 146 146 147 147 148 149 152 153 154 154 156 157 159 159 160 163 165 166 168 169 170 171 172 173 174 176 ---------------------------------------------------- tiii] ---------------------------------------------------
Table of Contents A regression approach to recommendations Combining multiple methods Basket analysis Obtaining useful predictions Analyzing supermarket shopping baskets Association ruie mining More advanced basket analysis Summary Chapter 8: Artificial Neural Networks and Deep Learning Using TensorFlow TensorFlow API Graphs Sessions Useful operations Saving and restoring neural networks Training neural networks Convolutional neural networks Recurrent neural networks LSTM for predicting text LSTM for image processing Summary Chapter 9: Classification II - Sentiment Analysis Sketching our roadmap Fetching the Twitter data Introducing the Naïve Bayes classifier Getting to know the Bayes theorem Being naive Using Naïve Bayes to classify Accounting for unseen words and other oddities Accounting for arithmetic underflows Creating our first classifier and tuning it Solving an easy problem first Using all classes Tuning the classifier s parameters Cleaning tweets Taking the word types into account Determining the word types Successfully cheating using SentiWordNet Our first estimator Putting everything together Summary Chapter 10: Topic Modeling --------------------------------------------------------- iso 182 ա 186 186 1Ց0 192 192 195 195 196 197 198 199 201 20Յ 204 214 215 219 221 223 223 224 224 225 226 227 230 230 233 234 237 239 244 246 246 248 250 253 254 255 [iv] ---------------------------------------------------------
Table of Contents Latent Dirichlet allocation 256 257 Building a topic model Comparing documents by topic Modeling the whole of Wikipedia Choosing the number of topics 262 265 268 Summary Chapter 11 : Classification III - Music Genre Classification Sketching our roadmap Fetching the music data Converting into WAV format 270 271 271 272 273 Looking at music 274 Decomposing music into sine-wave components Using FFT to build our first classifier Increasing experimentation agility Training the classifier Using a confusion matrix to measure accuracy in multiclass problems An alternative way to measure classifier performance using receiveroperator characteristics 276 278 278 279 281 284 Improving classification performance with mel frequency cepstral Coefficients 286 Music classification using Tensorflow Summary Chapter 12: Computer Vision Introducing image processing Loading and displaying images Thresholding Gaussian blurring Putting the center in focus 301 302 302 305 зоб зоб Basic image classification Computing features from images Writing your own features Using features to find similar images Classifying a harder dataset Local feature representations Image generation with adversarial networks Summary Chapter 13: Reinforcement Learning Types of reinforcement learning Policy and value network Q-network Excelling at games 310 311 312 314 316 318 321 ззо 331 331 332 333 ззз ззз A small example --------------------------------------------------------- 291 2ՑՑ [v] ---------------------------------------------------------
Table of Contents Using Tensorflow for the text game Playing breakout Summary Chapter 14: Bigger Data Learning about big data 335 338 349 Using jug to break up your pipeline into tasks An introduction to tasks in jug 351 352 352 353 Looking under the hood 356 Using jug for data analysis 357 Reusing partial results Յ60 Using Amazon Web Services Յ62 Creating your first virtuai machines 364 Installing Python packages on Amazon Linux 370 Running jug on our cioud machine 371 Automating the generation of clusters with cfncluster 372 Summary 376 Appendix A: Where to Learn More About Machine Learning___________ 379 Online COUrseS 379 Books 379 Blogs Data sources Getting competitive 380 Յ80 Յ80 All that was left out 381 Summary З81 Other Books You May Enjoy_____________________________________ 3 3 index________________________________________________________ [vi] 387
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| any_adam_object | 1 |
| author | Coelho, Luis Pedro Richert, Willi Brucher, Matthieu |
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| indexdate | 2024-07-10T08:16:19Z |
| institution | BVB |
| isbn | 9781788623223 |
| language | English |
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| spelling | Coelho, Luis Pedro Verfasser (DE-588)1075498678 aut Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow Luis Pedro Coelho, Willi Richert, Matthieu Brucher Third edition Birmingham ; Mumbai Packt 2018 vi, 392 pages Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Includes index Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Python (Computer program language) Machine learning Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Richert, Willi Verfasser (DE-588)127842950 aut Brucher, Matthieu Verfasser aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030757663&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Coelho, Luis Pedro Richert, Willi Brucher, Matthieu Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4434275-5 |
| title | Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow |
| title_auth | Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow |
| title_exact_search | Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow |
| title_full | Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow Luis Pedro Coelho, Willi Richert, Matthieu Brucher |
| title_fullStr | Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow Luis Pedro Coelho, Willi Richert, Matthieu Brucher |
| title_full_unstemmed | Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow Luis Pedro Coelho, Willi Richert, Matthieu Brucher |
| title_short | Building machine learning systems with Python |
| title_sort | building machine learning systems with python explore machine learning and deep learning techniques for building intelligent systems using scikit learn and tensorflow |
| title_sub | explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Maschinelles Lernen Python Programmiersprache |
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