Mastering Machine Learning on AWS
Dr. Saket S.R. Mengle Maximo Gurmendez更新时间：2021-06-24 14:23:51
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Title Page
Copyright and Credits
Mastering Machine Learning on AWS
Dedication
About Packt
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Contributors
About the authors
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
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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
Naïve 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
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更新时间：2021-06-24 14:23:51