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...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 approach to produce your trained ML models and port them to your mobile for easy access Who This Book Is For This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy What You Will Learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google's pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications In Detail Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of th... |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788398879 1788398874 |
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| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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 approach to produce your trained ML models and port them to your mobile for easy access Who This Book Is For This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy What You Will Learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google's pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications In Detail Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of th... | ||
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| 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. |
| ctrlnum | (OCoLC)1038280750 |
| dewey-full | 006.31 |
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ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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 approach to produce your trained ML models and port them to your mobile for easy access Who This Book Is For This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy What You Will Learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google's pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications In Detail Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of th...</subfield></datafield><datafield tag="610" ind1="2" ind2="0"><subfield code="a">Google (Firm)</subfield><subfield code="0">http://id.loc.gov/authorities/names/no00095539</subfield></datafield><datafield tag="610" ind1="2" ind2="7"><subfield code="a">Google (Firm)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Cloud computing.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh2008004883</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Apprentissage automatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Infonuagique.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Database design & theory.</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data capture & analysis.</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Information architecture.</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Artificial intelligence.</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Computers</subfield><subfield code="x">Data Processing.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Computers</subfield><subfield code="x">Data Modeling & Design.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Computers</subfield><subfield code="x">Intelligence (AI) & Semantics.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">General.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cloud computing</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ayyadevara, V. 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| id | ZDB-4-EBA-on1038280750 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:28:58Z |
| institution | BVB |
| isbn | 9781788398879 1788398874 |
| language | English |
| oclc_num | 1038280750 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (1 volume) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Ciaburro, Giuseppe, author. Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier. Birmingham, UK : Packt Publishing, 2018. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed May 30, 2018). 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. 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 approach to produce your trained ML models and port them to your mobile for easy access Who This Book Is For This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy What You Will Learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google's pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications In Detail Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of th... Google (Firm) http://id.loc.gov/authorities/names/no00095539 Google (Firm) fast Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Apprentissage automatique. Infonuagique. Database design & theory. bicssc Data capture & analysis. bicssc Information architecture. bicssc Artificial intelligence. bicssc Computers Data Processing. bisacsh Computers Data Modeling & Design. bisacsh Computers Intelligence (AI) & Semantics. bisacsh COMPUTERS General. bisacsh Cloud computing fast Machine learning fast Ayyadevara, V. Kishore, author. Perrier, Alexis, author. has work: Hands-on machine learning on Google cloud platform (Text) https://id.oclc.org/worldcat/entity/E39PCFwYpTVpgphdq93KGfvVbq https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1804697 Volltext |
| spellingShingle | Ciaburro, Giuseppe Ayyadevara, V. Kishore Perrier, Alexis Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / 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. Google (Firm) http://id.loc.gov/authorities/names/no00095539 Google (Firm) fast Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Apprentissage automatique. Infonuagique. Database design & theory. bicssc Data capture & analysis. bicssc Information architecture. bicssc Artificial intelligence. bicssc Computers Data Processing. bisacsh Computers Data Modeling & Design. bisacsh Computers Intelligence (AI) & Semantics. bisacsh COMPUTERS General. bisacsh Cloud computing fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/names/no00095539 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2008004883 |
| title | Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / |
| title_auth | Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / |
| title_exact_search | Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / |
| title_full | Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier. |
| title_fullStr | Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier. |
| title_full_unstemmed | Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier. |
| title_short | Hands-on machine learning on Google cloud platform : |
| title_sort | hands on machine learning on google cloud platform implementing smart and efficient analytics using cloud ml engine |
| title_sub | implementing smart and efficient analytics using Cloud ML Engine / |
| topic | Google (Firm) http://id.loc.gov/authorities/names/no00095539 Google (Firm) fast Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Apprentissage automatique. Infonuagique. Database design & theory. bicssc Data capture & analysis. bicssc Information architecture. bicssc Artificial intelligence. bicssc Computers Data Processing. bisacsh Computers Data Modeling & Design. bisacsh Computers Intelligence (AI) & Semantics. bisacsh COMPUTERS General. bisacsh Cloud computing fast Machine learning fast |
| topic_facet | Google (Firm) Machine learning. Cloud computing. Apprentissage automatique. Infonuagique. Database design & theory. Data capture & analysis. Information architecture. Artificial intelligence. Computers Data Processing. Computers Data Modeling & Design. Computers Intelligence (AI) & Semantics. COMPUTERS General. Cloud computing Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1804697 |
| work_keys_str_mv | AT ciaburrogiuseppe handsonmachinelearningongooglecloudplatformimplementingsmartandefficientanalyticsusingcloudmlengine AT ayyadevaravkishore handsonmachinelearningongooglecloudplatformimplementingsmartandefficientanalyticsusingcloudmlengine AT perrieralexis handsonmachinelearningongooglecloudplatformimplementingsmartandefficientanalyticsusingcloudmlengine |