Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier.
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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| Language: | English |
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Birmingham, UK :
Packt Publishing,
2018.
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Table of Contents:
- 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.