Building machine learning and deep learning models on Google Cloud Platform : a comprehensive guide for beginners /: a comprehensive guide for beginners. ([2019])

• Record Type: Book
• Further Information: Note: Ekaba Bisong.
• Authors: Bisong, Ekaba
• Contents: 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 Mach ine 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 -- .
• Publisher Details: New York : Apress
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource, illustrations (some color)
• Subjects: 006.3/1; Machine learning; Cloud computing; Computing platforms; Electronic books; Electronic books
• Languages: English
• ISBNs: 9781484244708; 1484244702
• Related ISBNs: 9781484244692
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed October 3, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.