Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine

Hands-on machine learning on Google cloud platform implementing smart and efficient analytics using Cloud ML Engine

Unleash Google's Cloud Platform to build, train and optimize machine learning models About This Book Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical...

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Detalles Bibliográficos
Otros Autores: Ciaburro, Giuseppe, author (author), Ayyadevara, V. Kishore, author, Perrier, Alexis, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt 2018.
Edición:First edition
Materias:
Google.
Machine learning.
Cloud computing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631742706719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Introducing the Google Cloud Platform
  • ML and the cloud
  • The nature of the cloud
  • Public cloud
  • Managed cloud versus unmanaged cloud
  • IaaS versus PaaS versus SaaS
  • Costs and pricing
  • ML
  • Introducing the GCP
  • Mapping the GCP
  • Getting started with GCP
  • Project-based organization
  • Creating your first project
  • Roles and permissions
  • Further reading
  • Summary
  • Chapter 2: Google Compute Engine
  • Google Compute Engine
  • VMs, disks, images, and snapshots
  • Creating a VM
  • Google Shell
  • Google Cloud Platform SDK
  • Gcloud
  • Gcloud config
  • Accessing your instance with gcloud
  • Transferring files with gcloud
  • Managing the VM
  • IPs
  • Setting up a data science stack on the VM
  • BOX the ipython console
  • Troubleshooting
  • Adding GPUs to instances
  • Startup scripts and stop scripts
  • Resources and further reading
  • Summary
  • Chapter 3: Google Cloud Storage
  • Google Cloud Storage
  • Box-storage versus drive
  • Accessing control lists
  • Access and management through the web console
  • gsutil
  • gsutil cheatsheet
  • Advanced gsutil
  • Signed URLs
  • Creating a bucket in Google Cloud Storage
  • Google Storage namespace
  • Naming a bucket
  • Naming an object
  • Creating a bucket
  • Google Cloud Storage console
  • Google Cloud Storage gsutil
  • Life cycle management
  • Google Cloud SQL
  • Databases supported
  • Google Cloud SQL performance and scalability
  • Google Cloud SQL security and architecture
  • Creating Google Cloud SQL instances
  • Summary
  • Chapter 4: Querying Your Data with BigQuery
  • Approaching big data
  • Data structuring
  • Querying the database
  • SQL basics
  • Google BigQuery
  • BigQuery basics
  • Using a graphical web UI
  • Visualizing data with Google Data Studio.
  • Creating reports in Data Studio
  • Summary
  • Chapter 5: Transforming Your Data
  • How to clean and prepare the data
  • Google Cloud Dataprep
  • Exploring Dataprep console
  • Removing empty cells
  • Replacing incorrect values
  • Mismatched values
  • Finding outliers in the data
  • Visual functionality
  • Statistical information
  • Removing outliers
  • Run Job
  • Scale of features
  • Min-max normalization
  • z score standardization
  • Google Cloud Dataflow
  • Summary
  • Chapter 6: Essential Machine Learning
  • Applications of machine learning
  • Financial services
  • Retail industry
  • Telecom industry
  • Supervised and unsupervised machine learning
  • Overview of machine learning techniques
  • Objective function in regression
  • Linear regression
  • Decision tree
  • Random forest
  • Gradient boosting
  • Neural network
  • Logistic regression
  • Objective function in classification
  • Data splitting
  • Measuring the accuracy of a model
  • Absolute error
  • Root mean square error
  • The difference between machine learning and deep learning
  • Applications of deep learning
  • Summary
  • Chapter 7: Google Machine Learning APIs
  • Vision API
  • Enabling the API
  • Opening an instance
  • Creating an instance using Cloud Shell
  • Label detection
  • Text detection
  • Logo detection
  • Landmark detection
  • Cloud Translation API
  • Enabling the API
  • Natural Language API
  • Speech-to-text API
  • Video Intelligence API
  • Summary
  • Chapter 8: Creating ML Applications with Firebase
  • Features of Firebase
  • Building a web application
  • Building a mobile application
  • Summary
  • Chapter 9: Neural Networks with TensorFlow and Keras
  • Overview of a neural network
  • Setting up Google Cloud Datalab
  • Installing and importing the required packages
  • Working details of a simple neural network
  • Backpropagation
  • Implementing a simple neural network in Keras.
  • Understanding the various loss functions
  • Softmax activation
  • Building a more complex network in Keras
  • Activation functions
  • Optimizers
  • Increasing the depth of network
  • Impact on change in batch size
  • Implementing neural networks in TensorFlow
  • Using premade estimators
  • Creating custom estimators
  • Summary
  • Chapter 10: Evaluating Results with TensorBoard
  • Setting up TensorBoard
  • Overview of summary operations
  • Ways to debug the code
  • Setting up TensorBoard from TensorFlow
  • Summaries from custom estimator
  • Summary
  • Chapter 11: Optimizing the Model through Hyperparameter Tuning
  • The intuition of hyperparameter tuning
  • Overview of hyperparameter tuning
  • Hyperparameter tuning in Google Cloud
  • The model file
  • Configuration file
  • Setup file
  • The __init__ file
  • Summary
  • Chapter 12: Preventing Overfitting with Regularization
  • Intuition of over/under fitting
  • Reducing overfitting
  • Implementing L2 regularization
  • Implementing L1 regularization
  • Implementing dropout
  • Reducing underfitting
  • Summary
  • Chapter 13: Beyond Feedforward Networks - CNN and RNN
  • Convolutional neural networks
  • Convolution layer
  • Rectified Linear Units
  • Pooling layers
  • Fully connected layer
  • Structure of a CNN
  • TensorFlow overview
  • Handwriting Recognition using CNN and TensorFlow
  • Run Python code on Google Cloud Shell
  • Recurrent neural network
  • Fully recurrent neural networks
  • Recursive neural networks
  • Hopfield recurrent neural networks
  • Elman neural networks
  • Long short-term memory networks
  • Handwriting Recognition using RNN and TensorFlow
  • LSTM on Google Cloud Shell
  • Summary
  • Chapter 14: Time Series with LSTMs
  • Introducing time series
  • Classical approach to time series
  • Estimation of the trend component
  • Estimating the seasonality component
  • Time series models.
  • Autoregressive models
  • Moving average models
  • Autoregressive moving average model
  • Autoregressive integrated moving average models
  • Removing seasonality from a time series
  • Analyzing a time series dataset
  • Identifying a trend in a time series
  • Time series decomposition
  • Additive method
  • Multiplicative method
  • LSTM for time series analysis
  • Overview of the time series dataset
  • Data scaling
  • Data splitting
  • Building the model
  • Making predictions
  • Summary
  • Chapter 15: Reinforcement Learning
  • Reinforcement learning introduction
  • Agent-Environment interface
  • Markov Decision Process
  • Discounted cumulative reward
  • Exploration versus exploitation
  • Reinforcement learning techniques
  • Q-learning
  • Temporal difference learning
  • Dynamic Programming
  • Monte Carlo methods
  • Deep Q-Network
  • OpenAI Gym
  • Cart-Pole system
  • Learning phase
  • Testing phase
  • Summary
  • Chapter 16: Generative Neural Networks
  • Unsupervised learning
  • Generative models
  • Restricted Boltzmann machine
  • Boltzmann machine architecture
  • Boltzmann machine disadvantages
  • Deep Boltzmann machines
  • Autoencoder
  • Variational autoencoder
  • Generative adversarial network
  • Adversarial autoencoder
  • Feature extraction using RBM
  • Breast cancer dataset
  • Data preparation
  • Model fitting
  • Autoencoder with Keras
  • Load data
  • Keras model overview
  • Sequential model
  • Keras functional API
  • Define model architecture
  • Magenta
  • The NSynth dataset
  • Summary
  • Chapter 17: Chatbots
  • Chatbots fundamentals
  • Chatbot history
  • The imitation game
  • Eliza
  • Parry
  • Jabberwacky
  • Dr. Sbaitso
  • ALICE
  • SmarterChild
  • IBM Watson
  • Building a bot
  • Intents
  • Entities
  • Context
  • Chatbots
  • Essential requirements
  • The importance of the text
  • Word transposition
  • Checking a value against a pattern.
  • Maintaining context
  • Chatbots architecture
  • Natural language processing
  • Natural language understanding
  • Google Cloud Dialogflow
  • Dialogflow overview
  • Basics Dialogflow elements
  • Agents
  • Intent
  • Entity
  • Action
  • Context
  • Building a chatbot with Dialogflow
  • Agent creation
  • Intent definition
  • Summary
  • Index.

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