Building Machine Learning and Deep Learning Models on Google Cloud Platform A Comprehensive Guide for Beginners /

• Κύριος συγγραφέας: Bisong, Ekaba (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Artificial intelligence.; Big data.; Artificial Intelligence.; Big Data.

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational...

Πίνακας περιεχομένων:

• Part 1: Getting Started with Google Cloud Platform
• Chapter 1: What Is Cloud Computing?
• Chapter 2: An Overview of Google Cloud Platform Services
• Chapter 3: The Google Cloud SDK and Web CLI
• Chapter 4: Google Cloud Storage (GCS)
• Chapter 5: Google Compute Engine (GCE)
• Chapter 6: JupyterLab Notebooks
• Chapter 7: Google Colaboratory
• Part 2: Programming Foundations for Data Science
• Chapter 8: What is Data Science?
• Chapter 9: Python
• Chapter 10: Numpy
• Chapter 11: Pandas
• Chapter 12: Matplotlib and Seaborn
• Part 3: Introducing Machine Learning
• Chapter 13: What Is Machine Learning?
• Chapter 14: Principles of Learning
• Chapter 15: Batch vs. Online Learning
• Chapter 16: Optimization for Machine Learning: Gradient Descent
• Chapter 17: Learning Algorithms
• Part 4: Machine Learning in Practice
• Chapter 18: Introduction to Scikit-learn
• Chapter 19: Linear Regression
• Chapter 20: Logistic Regression
• Chapter 21: Regularization for Linear Models
• Chapter 22: Support Vector Machines
• Chapter 23: Ensemble Methods
• Chapter 24: More Supervised Machine Learning Techniques with Scikit-learn
• Chapter 25: Clustering
• Chapter 26: Principal Components Analysis (PCA)
• Part 5: Introducing Deep Learning
• Chapter 27: What is Deep Learning?
• Chapter 28: Neural Network Foundations
• Chapter 29: Training a Neural Network
• Part 6: Deep Learning in Practice
• Chapter 30: TensorFlow 2.0 and Keras
• Chapter 31: The Multilayer Perceptron (MLP)
• Chapter 32: Other Considerations for Training the Network
• Chapter 33: More on Optimization Techniques
• Chapter 34: Regularization for Deep Learning
• Chapter 35: Convolutional Neural Networks (CNN)
• Chapter 36: Recurrent Neural Networks (RNN)
• Chapter 37: Autoencoders
• Part 7: Advanced Analytics/ Machine Learning on Google Cloud Platform
• Chapter 38: Google BigQuery
• Chapter 39: Google Cloud Dataprep
• Chapter 40: Google Cloud Dataflow
• Chapter 41: Google Cloud Machine Learning Engine (Cloud MLE)
• Chapter 42: Google AutoML: Cloud Vision
• Chapter 43: Google AutoML: Cloud Natural Language Processing
• Chapter 44: Model to Predict the Critical Temperature of Superconductors
• Part 8: Productionalizing Machine Learning Solutions on GCP
• Chapter 45: Containers and Google Kubernetes Engine
• Chapter 46: Kubeflow and Kubeflow Pipelines
• Chapter 47: Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines
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