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Building Machine Learning Systems with Python
Luis Pedro Coelho Willi Richert Matthieu Brucher 著
更新時(shí)間：2021-07-23 17:12:06
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Machinelearningallowssystemstolearnthingswithoutbeingexplicitlyprogrammedtodoso.Pythonisoneofthemostpopularlanguagesusedtodevelopmachinelearningapplications,whichtakeadvantageofitsextensivelibrarysupport.ThisthirdeditionofBuildingMachineLearningSystemswithPythonaddressesrecentdevelopmentsinthefieldbycoveringthemost-useddatasetsandlibrariestohelpyoubuildpracticalmachinelearningsystems.Usingmachinelearningtogaindeeperinsightsfromdataisakeyskillrequiredbymodernapplicationdevelopersandanalystsalike.Python,beingadynamiclanguage,allowsforfastexplorationandexperimentation.Thisbookshowsyouexactlyhowtofindpatternsinyourrawdata.YouwillstartbybrushinguponyourPythonmachinelearningknowledgeandbeingintroducedtolibraries.You'llquicklygettogripswithserious,real-worldprojectsondatasets,usingmodelingandcreatingrecommendationsystems.WithBuildingMachineLearningSystemswithPython,you’llgainthetoolsandunderstandingrequiredtobuildyourownsystems,alltailoredtosolvereal-worlddataanalysisproblems.Bytheendofthisbook,youwillbeabletobuildmachinelearningsystemsusingtechniquesandmethodologiessuchasclassification,sentimentanalysis,computervision,reinforcementlearning,andneuralnetworks.
最新章節(jié)
- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- Summary
- All that was left out
- Getting competitive
- Data sources
品牌：中圖公司
上架時(shí)間：2021-07-23 15:49:34
出版社：Packt Publishing
本書(shū)數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Leave a review - let other readers know what you think 更新時(shí)間：2021-07-23 17:12:06
- Other Books You May Enjoy
- Summary
- All that was left out
- Getting competitive
- Data sources
- Blogs
- Books
- Online courses
- Where to Learn More About Machine Learning
- Summary
- Automating the generation of clusters with cfncluster
- Running jug on our cloud machine
- Installing Python packages on Amazon Linux
- Creating your first virtual machines
- Using Amazon Web Services
- Reusing partial results
- Using jug for data analysis
- Looking under the hood
- An introduction to tasks in jug
- Using jug to break up your pipeline into tasks
- Learning about big data
- Bigger Data
- Summary
- Playing breakout
- Using Tensorflow for the text game
- A small example
- Excelling at games
- Q-network
- Policy and value network
- Types of reinforcement learning
- Reinforcement Learning
- Summary
- Image generation with adversarial networks
- Local feature representations
- Classifying a harder dataset
- Using features to find similar images
- Writing your own features
- Computing features from images
- Basic image classification
- Putting the center in focus
- Gaussian blurring
- Thresholding
- Loading and displaying images
- Introducing image processing
- Computer Vision
- Summary
- Music classification using Tensorflow
- Improving classification performance with mel frequency cepstral coefficients
- An alternative way to measure classifier performance using receiver-operator characteristics
- Using a confusion matrix to measure accuracy in multiclass problems
- Training the classifier
- Increasing experimentation agility
- Using FFT to build our first classifier
- Decomposing music into sine-wave components
- Looking at music
- Converting into WAV format
- Fetching the music data
- Sketching our roadmap
- Classification III – Music Genre Classification
- Summary
- Choosing the number of topics
- Modeling the whole of Wikipedia
- Comparing documents by topic
- Building a topic model
- Latent Dirichlet allocation
- Topic Modeling
- Summary
- Putting everything together
- Our first estimator
- Successfully cheating using SentiWordNet
- Determining the word types
- Taking the word types into account
- Cleaning tweets
- Tuning the classifier's parameters
- Using all classes
- Solving an easy problem first
- Creating our first classifier and tuning it
- Accounting for arithmetic underflows
- Accounting for unseen words and other oddities
- Using Na?ve Bayes to classify
- Being na?ve
- Getting to know the Bayes theorem
- Introducing the Na?ve Bayes classifier
- Fetching the Twitter data
- Sketching our roadmap
- Classification II – Sentiment Analysis
- Summary
- LSTM for image processing
- LSTM for predicting text
- Recurrent neural networks
- Convolutional neural networks
- Training neural networks
- Saving and restoring neural networks
- Useful operations
- Sessions
- Graphs
- TensorFlow API
- Using TensorFlow
- Artificial Neural Networks and Deep Learning
- Summary
- More advanced basket analysis
- Association rule mining
- Analyzing supermarket shopping baskets
- Obtaining useful predictions
- Basket analysis
- Combining multiple methods
- A regression approach to recommendations
- A neighborhood approach to recommendations
- Normalizing the training data
- Splitting into training and testing
- Rating predictions and recommendations
- Recommendations
- Summary
- Tweaking the parameters
- Another look at noise
- Solving our initial challenge
- Clustering posts
- Getting test data to evaluate our ideas
- K-means
- Clustering
- Our achievements and goals
- Stop words on steroids
- Extending the vectorizer with NLTK's stemmer
- Installing and using NLTK
- Stemming
- Removing less important words
- Normalizing word count vectors
- Counting words
- Converting raw text into a bag of words
- Preprocessing – similarity measured as a similar number of common words
- How to do it
- How not to do it
- Measuring the relatedness of posts
- Clustering – Finding Related Posts
- Summary
- Autoencoders or neural networks for dimensionality reduction
- Multidimensional scaling
- Limitations of PCA and how LDA can help
- Applying PCA
- Sketching PCA
- Principal component analysis
- Feature projection
- Other feature selection methods
- Asking the model about the features using wrappers
- Mutual information
- Correlation
- Detecting redundant features using filters
- Selecting features
- Sketching our roadmap
- Dimensionality Reduction
- Summary
- Classification using Tensorflow
- Ship it!
- Slimming the classifier
- Looking behind accuracy – precision and recall
- Applying logistic regression to our post-classification problem
- A bit of math with a small example
- Using logistic regression
- High or low bias?
- Fixing high variance
- Fixing high bias
- Bias variance and their trade-off
- Deciding how to improve the performance
- Designing more features
- Measuring the classifier's performance
- Training the classifier
- Engineering the features
- Creating our first classifier
- Defining what a good answer is
- Preselecting and processing attributes
- Slimming the data down to chewable chunks
- Fetching the data
- Tuning the classifier
- Tuning the instance
- Learning to classify classy answers
- Sketching our roadmap
- Classification I – Detecting Poor Answers
- Summary
- Regression with TensorFlow
- Setting hyperparameters in a principled way
- An example based on text documents
- P-greater-than-N scenarios
- Visualizing the Lasso path
- Using Lasso or ElasticNet in scikit-learn
- L1 and L2 penalties
- Penalized or regularized regression
- Cross-validation for regression
- Multidimensional regression
- Predicting house prices with regression
- Regression
- Summary
- Which classifier to use
- Looking at the decision boundaries
- Nearest neighbor classification
- Features and feature engineering
- Learning about the seeds dataset
- A more complex dataset and the nearest-neighbor classifier
- How to measure and compare classifiers
- Evaluation – holding out data and cross-validation
- Building our first classification model
- Classifying with scikit-learn
- Visualization is a good first step
- The Iris dataset
- Classifying with Real-World Examples
- Summary
- Answering our initial question
- Training and testing
- Stepping back to go forward - another look at our data
- Toward more complex models
- Starting with a simple straight line
- Before we build our first model
- Choosing the right model and learning algorithm
- Preprocessing and cleaning the data
- Reading in the data
- Our first (tiny) application of machine learning
- Getting answers
- Asking a question
- Fundamentals of machine learning
- Learning SciPy
- Comparing the runtime
- Handling nonexistent values
- Indexing
- Learning NumPy
- Chewing data efficiently with NumPy and intelligently with SciPy
- Installing Python
- Introduction to NumPy SciPy Matplotlib and TensorFlow
- Getting started
- What to do when you are stuck
- How to best read this book
- What the book will teach you – and what it will not
- Machine learning and Python – a dream team
- Getting Started with Python Machine Learning
- Reviews
- Get in touch
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewers
- About the authors
- Contributors
- PacktPub.com
- Why subscribe?
- Packt Upsell
- Building Machine Learning Systems with Python Third Edition
- Copyright and Credits
- Title Page
- 封面
- 封面
- Title Page
- Copyright and Credits
- Building Machine Learning Systems with Python Third Edition
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the authors
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- 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
- 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
- Which classifier to use
- Summary
- Regression
- Predicting house prices with regression
- Multidimensional regression
- Cross-validation for regression
- Penalized or regularized regression
- L1 and L2 penalties
- 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
- 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?
- Using logistic regression
- 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 it!
- Classification using Tensorflow
- Summary
- 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
- Clustering – Finding Related Posts
- Measuring the relatedness of posts
- How not to do it
- How to do it
- 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 NLTK's stemmer
- Stop words on steroids
- Our achievements and goals
- Clustering
- K-means
- Getting test data to evaluate our ideas
- Clustering posts
- Solving our initial challenge
- Another look at noise
- Tweaking the parameters
- Summary
- Recommendations
- Rating predictions and recommendations
- Splitting into training and testing
- Normalizing the training data
- A neighborhood approach to recommendations
- A regression approach to recommendations
- Combining multiple methods
- Basket analysis
- Obtaining useful predictions
- Analyzing supermarket shopping baskets
- Association rule mining
- More advanced basket analysis
- Summary
- 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
- Classification II – Sentiment Analysis
- Sketching our roadmap
- Fetching the Twitter data
- Introducing the Na?ve Bayes classifier
- Getting to know the Bayes theorem
- Being na?ve
- Using Na?ve 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
- Topic Modeling
- Latent Dirichlet allocation
- Building a topic model
- Comparing documents by topic
- Modeling the whole of Wikipedia
- Choosing the number of topics
- Summary
- Classification III – Music Genre Classification
- Sketching our roadmap
- Fetching the music data
- Converting into WAV format
- Looking at music
- 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 receiver-operator characteristics
- Improving classification performance with mel frequency cepstral coefficients
- Music classification using Tensorflow
- Summary
- Computer Vision
- Introducing image processing
- Loading and displaying images
- Thresholding
- Gaussian blurring
- Putting the center in focus
- 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
- Reinforcement Learning
- Types of reinforcement learning
- Policy and value network
- Q-network
- Excelling at games
- A small example
- Using Tensorflow for the text game
- Playing breakout
- Summary
- Bigger Data
- Learning about big data
- Using jug to break up your pipeline into tasks
- An introduction to tasks in jug
- Looking under the hood
- Using jug for data analysis
- Reusing partial results
- Using Amazon Web Services
- Creating your first virtual machines
- Installing Python packages on Amazon Linux
- Running jug on our cloud machine
- Automating the generation of clusters with cfncluster
- Summary
- Where to Learn More About Machine Learning
- Online courses
- Books
- Blogs
- Data sources
- Getting competitive
- All that was left out
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-07-23 17:12:06
