Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners - Tapa blanda

Sinopsis

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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