دانلود کتاب Hands-On Artificial Intelligence on Google Cloud Platform: Build intelligent applications powered by TensorFlow, Cloud AutoML, BigQuery, and Dialogflow

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Cover Title Page About Packt Copyright and Credits Contributors Table of Contents Preface Section 1: Basics of Google Cloud Platform Chapter 1: Overview of AI and GCP Understanding the Cloud First strategy for advanced data analytics Advantages of a Cloud First strategy Anti-patterns of the Cloud First strategy  Google data centers Overview of GCP AI building blocks Data Storage Processing Actions Natural language processing  Speech recognition Machine vision Information processing and reasoning Planning and exploring Handling and control Navigation and movement Speech generation Image generation AI tools available on GCP Sight Language Conversation Summary Chapter 2: Computing and Processing Using GCP Components Understanding the compute options Compute Engine Compute Engine and AI applications App Engine App Engine and AI applications Cloud Functions Cloud Functions and AI applications Kubernetes Engine Kubernetes Engine and AI applications Diving into the storage options Cloud Storage Cloud Storage and AI applications Cloud Bigtable Cloud Bigtable and AI applications Cloud Datastore Cloud Datastore and AI applications Cloud Firestore Cloud Firestore and AI applications Cloud SQL Cloud SQL and AI applications Cloud Spanner Cloud Spanner and AI applications Cloud Memorystore Cloud Memorystore and AI applications Cloud Filestore Cloud Filestore and AI applications Understanding the processing options BigQuery BigQuery and AI applications Cloud Dataproc Cloud Dataproc and AI applications Cloud Dataflow Cloud Dataflow and AI applications Building an ML pipeline  Understanding the flow design Loading data into Cloud Storage Loading data to BigQuery Training the model Evaluating the model Testing the model Summary Section 2: Artificial Intelligence with Google Cloud Platform Chapter 3: Machine Learning Applications with XGBoost Overview of the XGBoost library Ensemble learning How does ensemble learning decide on the optimal predictive model? Reducible errors – bias Reducible errors – variance Irreducible errors Total error Gradient boosting eXtreme Gradient Boosting (XGBoost) Training and storing XGBoost machine learning models Using XGBoost trained models Building a recommendation system using the XGBoost library Creating and testing the XGBoost recommendation system model  Summary Chapter 4: Using Cloud AutoML Overview of Cloud AutoML  The workings of AutoML AutoML API overview REST source – pointing to model locations REST source – for evaluating the model REST source – the operations API Document classification using AutoML Natural Language The traditional machine learning approach for document classification Document classification with AutoML Navigating to the AutoML Natural Language interface Creating the dataset Labeling the training data Training the model Evaluating the model The command line Python Java Node.js Using the model for predictions The web interface A REST API for model predictions Python code for model predictions Image classification using AutoML Vision APIs Image classification steps with AutoML Vision  Collecting training images Creating a dataset Labeling and uploading training images Training the model Evaluating the model The command-line interface Python code Testing the model Python code Performing speech-to-text conversion using the Speech-to-Text API Synchronous requests Asynchronous requests Streaming requests Sentiment analysis using AutoML Natural Language APIs Summary Chapter 5: Building a Big Data Cloud Machine Learning Engine Understanding ML Understanding how to use Cloud Machine Learning Engine Google Cloud AI Platform Notebooks Google AI Platform deep learning images Creating Google Platform AI Notebooks Using Google Platform AI Notebooks Automating AI Notebooks execution Overview of the Keras framework  Training your model using the Keras framework Training your model using Google AI Platform Asynchronous batch prediction using Cloud Machine Learning Engine Real-time prediction using Cloud Machine Learning Engine Summary Chapter 6: Smart Conversational Applications Using DialogFlow Introduction to DialogFlow Understanding the building blocks of DialogFlow Building a DialogFlow agent Use cases supported by DialogFlow Performing audio sentiment analysis using DialogFlow Summary Section 3: TensorFlow on Google Cloud Platform Chapter 7: Understanding Cloud TPUs Introducing Cloud TPUs and their organization Advantages of using TPUs Mapping of software and hardware architecture Available TPU versions Performance benefits of TPU v3 over TPU v2 Available TPU configurations Software architecture Best practices of model development using TPUs Guiding principles for model development on a TPU Training your model using TPUEstimator Standard TensorFlow Estimator API TPUEstimator programming model TPUEstimator concepts Converting from TensorFlow Estimator to TPUEstimator Setting up TensorBoard for analyzing TPU performance Performance guide XLA compiler performance Consequences of tiling Fusion Understanding preemptible TPUs Steps for creating a preemptible TPU from the console Preemptible TPU pricing Preemptible TPU detection  Summary Chapter 8: Implementing TensorFlow Models Using Cloud ML Engine Understanding the components of Cloud ML Engine Training service Using the built-in algorithms Using a custom training application Prediction service Notebooks Data Labeling Service Deep learning containers Steps involved in training and utilizing a TensorFlow model Prerequisites Creating a TensorFlow application and running it locally Project structure recommendation Training data Packaging and deploying your training application in Cloud ML Engine Choosing the right compute options for your training job Choosing the hyperparameters for the training job Monitoring your TensorFlow training model jobs Summary Chapter 9: Building Prediction Applications Overview of machine-based intelligent predictions Understanding the prediction process Maintaining models and their versions Taking a deep dive into saved models SignatureDef in the TensorFlow SavedModel TensorFlow SavedModel APIs Deploying the models on GCP Uploading saved models to a Google Cloud Storage bucket Testing machine learning models Deploying models and their version Model training example Performing prediction with service endpoints Summary Section 4: Building Applications and Upcoming Features Chapter 10: Building an AI application A step-by-step approach to developing AI applications Problem classification  Classification Regression Clustering Optimization Anomaly detection Ranking Data preparation Data acquisition  Data processing  Problem modeling  Validation and execution Holdout Cross-validation Model evaluation parameters (metrics) Classification metrics Model deployment Overview of the use case – automated invoice processing (AIP) Designing AIP with AI platform tools on GCP Performing optical character recognition using the Vision API Storing the invoice with Cloud SQL Creating a Cloud SQL instance Setting up the database and tables Enabling the Cloud SQL API  Enabling the Cloud Functions API  Creating a Cloud Function  Providing the Cloud SQL Admin role Validating the invoice with Cloud Functions Scheduling the invoice for the payment queue (pub/sub) Notifying the vendor and AP team about the payment completion Creating conversational interface for AIP Upcoming features Summary Other Books You May Enjoy Index