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Mastering Machine Learning on AWS
AWSisconstantlydrivingnewinnovationsthatempowerdatascientiststoexploreavarietyofmachinelearning(ML)cloudservices.ThisbookisyourcomprehensivereferenceforlearningandimplementingadvancedMLalgorithmsinAWScloud.Asyougothroughthechapters,you’llgaininsightsintohowthesealgorithmscanbetrained,tunedanddeployedinAWSusingApacheSparkonElasticMapReduce(EMR),SageMaker,andTensorFlow.WhileyoufocusonalgorithmssuchasXGBoost,linearmodels,factorizationmachines,anddeepnets,thebookwillalsoprovideyouwithanoverviewofAWSaswellasdetailedpracticalapplicationsthatwillhelpyousolvereal-worldproblems.EverypracticalapplicationincludesaseriesofcompanionnotebookswithallthenecessarycodetorunonAWS.Inthenextfewchapters,youwilllearntouseSageMakerandEMRNotebookstoperformarangeoftasks,rightfromsmartanalytics,andpredictivemodeling,throughtosentimentanalysis.Bytheendofthisbook,youwillbeequippedwiththeskillsyouneedtoeffectivelyhandlemachinelearningprojectsandimplementandevaluatealgorithmsonAWS.
目錄(232章)
倒序
- coverpage
- Copyright and Credits
- Mastering Machine Learning on AWS
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- About the reviewer
- 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
- Section 1: Machine Learning on AWS
- Getting Started with Machine Learning for AWS
- How AWS empowers data scientists
- Using AWS tools for ML
- Identifying candidate problems that can be solved using ML
- The ML project life cycle
- Data gathering
- Evaluation metrics
- Algorithm selection
- Deploying models
- Summary
- Exercises
- Section 2: Implementing Machine Learning Algorithms at Scale on AWS
- Classifying Twitter Feeds with Naive Bayes
- Classification algorithms
- Feature types
- Nominal features
- Ordinal features
- Continuous features
- Naive Bayes classifier
- Bayes' theorem
- Posterior
- Likelihood
- Prior probability
- Evidence
- How the Naive Bayes algorithm works
- Classifying text with language models
- Collecting the tweets
- Preparing the data
- Building a Naive Bayes model through SageMaker notebooks
- Na?ve Bayes model on SageMaker notebooks using Apache Spark
- Using SageMaker's BlazingText built-in ML service
- Naive Bayes – pros and cons
- Summary
- Exercises
- Predicting House Value with Regression Algorithms
- Predicting the price of houses
- Understanding linear regression
- Linear least squares estimation
- Maximum likelihood estimation
- Gradient descent
- Evaluating regression models
- Mean absolute error
- Mean squared error
- Root mean squared error
- R-squared
- Implementing linear regression through scikit-learn
- Implementing linear regression through Apache Spark
- Implementing linear regression through SageMaker's Linear Learner
- Understanding logistic regression
- Logistic regression in Spark
- Pros and cons of linear models
- Summary
- Predicting User Behavior with Tree-Based Methods
- Understanding decision trees
- Recursive splitting
- Types of decision trees
- Cost functions
- Gini Impurity
- Information gain
- Criteria to stop splitting trees
- Understanding random forest algorithms
- Understanding gradient-boosting algorithms
- Predicting clicks on log streams
- Introduction to Elastic MapReduce (EMR)
- Training with Apache Spark on EMR
- Getting the data
- Preparing the data
- Categorical encoding
- One-hot encoding
- Training a model
- Evaluating our model
- Area under the ROC curve
- Area under the precision-recall curve
- Training tree ensembles on EMR
- Training gradient-boosted trees with the SageMaker services
- Preparing the data
- Training with SageMaker XGBoost
- Applying and evaluating the model
- Summary
- Exercises
- Customer Segmentation Using Clustering Algorithms
- Understanding how clustering algorithms work
- k-means clustering
- Euclidean distance
- Manhattan distance
- Hierarchical clustering
- Agglomerative clustering
- Divisive clustering
- Clustering with Apache Spark on EMR
- Clustering with Spark and SageMaker on EMR
- Understanding the purpose of the IAM role
- Summary
- Exercises
- Analyzing Visitor Patterns to Make Recommendations
- Making theme park attraction recommendations through Flickr data
- Collaborative filtering
- Memory-based approach
- Model-based approach
- Matrix factorization
- Stochastic gradient descent
- Alternating least squares
- Finding recommendations through Apache Spark's ALS
- Data gathering and exploration
- Training the model
- Getting recommendations
- Recommending attractions through SageMaker FMs
- Preparing the dataset for learning
- Training the model
- Getting recommendations
- Summary
- Exercises
- Section 3: Deep Learning
- Implementing Deep Learning Algorithms
- Understanding deep learning
- Applications of deep learning
- Self-driving cars
- Learning to play video games using a deep learning algorithm
- Understanding deep learning algorithms
- Neural network algorithms
- Activation functions
- Backpropagation
- Introduction to deep neural networks
- Understanding convolutional neural networks
- Summary
- Exercises
- Implementing Deep Learning with TensorFlow on AWS
- Introducing TensorFlow
- TensorFlow as a general machine learning library
- Training and serving the TensorFlow model through SageMaker
- Creating a custom neural net with TensorFlow
- Summary
- Exercises
- Image Classification and Detection with SageMaker
- Introducing Amazon SageMaker for image classification
- Training a deep learning model using Amazon SageMaker
- Classifying images using Amazon SageMaker
- Summary
- Exercises
- Section 4: Integrating Ready-Made AWS Machine Learning Services
- Working with AWS Comprehend
- Introducing Amazon Comprehend
- Accessing Amazon Comprehend
- Named-entity recognition using Comprehend
- Sentiment analysis using Comprehend
- Text classification using Comprehend
- Summary
- Exercises
- Using AWS Rekognition
- Introducing Amazon Rekognition
- Implementing object and scene detection
- Implementing facial analysis
- Other Rekognition services
- Image moderation
- Celebrity recognition
- Face comparison
- Summary
- Exercises
- Building Conversational Interfaces Using AWS Lex
- Introducing Amazon Lex
- Building a custom chatbot using Amazon Lex
- Summary
- Exercises
- Section 5: Optimizing and Deploying Models through AWS
- Creating Clusters on AWS
- Choosing your instance types
- On-demand versus spot instance pricing
- Reserved pricing
- Amazon Machine Images (AMIs)
- Deep learning hardware
- Distributed deep learning
- Model parallelization versus data parallelization
- Distributed TensorFlow
- Distributed learning through Apache Spark
- Data parallelization
- Model parallelization
- Distributed hyperparameter tuning
- Distributed predictions at scale
- Parallelization in SageMaker
- Summary
- Optimizing Models in Spark and SageMaker
- The importance of model optimization
- Automatic hyperparameter tuning
- Hyperparameter tuning in Apache Spark
- Hyperparameter tuning in SageMaker
- Summary
- Exercises
- Tuning Clusters for Machine Learning
- Introduction to the EMR architecture
- Apache Hadoop
- Apache Spark
- Apache Hive
- Presto
- Apache HBase
- Yet Another Resource Negotiator (YARN)
- Tuning EMR for different applications
- Configuring application properties
- Maximize Resource Allocation
- The AWS Glue Catalog
- Managing data pipelines with Glue
- Creating tables with Glue
- Accessing Glue tables in Spark
- Summary
- Deploying Models Built in AWS
- SageMaker model deployment
- Apache Spark model deployment
- Summary
- Exercises
- Appendix: Getting Started with AWS
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 14:23:51
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