Art and Science of Machine Learning
Welcome to the Art and Science of Machine Learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
What you'll learn
Welcome to the Art and Science of Machine Learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
Table of contents
- Introduction 2m
- Regularization 5m
- L1 & L2 Regularizations 5m
- Lab Intro: Regularization 0m
- Getting Started With GCP And Qwiklabs 4m
- Lab: Regularization 3m
- Resources Readings - 1 - The Art of ML (The Art of ML) 0m
- Learning Rate and Batch Size 5m
- Optimization 1m
- Lab Intro: Reviewing Learning Curves 1m
- Lab: Reviewing Learning Curve 0m
- Resources Readings - 2 - The Art of ML (Learning Rate and Batch Size) 0m
- Introduction 1m
- Parameters vs Hyperparameters 2m
- Think Beyond Grid Search 3m
- Lab Intro: Export Data from BigQuery to Google Cloud Storage 0m
- Lab: Exporting data from BigQuery to Cloud Storage 0m
- Lab Intro: Performing Hyperparameter Tuning 1m
- Lab: Performing the Hyperparameter Tuning 0m
- Resources Readings - 3 - Hyperparameter Tuning 0m
- Introduction to Neural Networks 1m
- Neural Networks 19m
- Lab: Neural Networks Playground 13m
- Training Neural Networks 14m
- Lab Intro: Build a DNN using the Keras Functional API 1m
- Lab: Build a DNN using the Keras Functional API 0m
- Lab Intro: Training Models at Scale with AI Platform 1m
- Lab: Training Models at Scale with AI Platform 0m
- Multi-class Neural Networks 11m
- Resources Readings - 6 - The Science of Neural Networks 0m
