Description
In this course, you will learn:
- How to create a GCP project and access it through the Cloud SDK utility
- TensorFlow development in detail, starting with basic tensor operations and proceeding to graphs, sessions, variables, and training.
- High-level features like datasets, iterators, and estimators. Next, Matt introduces the Google Cloud Platform (GCP) and its capabilities.
- How to deploy your TensorFlow applications to the ML Engine.
Syllabus:
- Introduction
- What you should know
- Using the exercise files
1. Introducing TensorFlow
- Overview and installation
- Getting started
- Running a simple application
2. Fundamentals of TensorFlow Development
- Creating tensors
- Basic tensor operations
- Advanced tensor operations
- Understanding graphs and sessions
- Accessing graphs and sessions in code
3. Training TensorFlow Applications
- Variables and logging
- Using variables in code
- Using optimizers
- Simple optimizer example
- Batches and placeholders
- Linear regression in code: Part 1
- Linear regression in code: Part 2
- TensorBoard
- Using TensorBoard in practice
4. Accessing Data with Datasets
- Datasets and iterators
- Coding with datasets and iterators
- Dataset operations
- Creating datasets from files
- Introducing MNIST images
- Reading MNIST data in code
5. Machine Learning with Estimators
- Understanding estimators
- Describing data with feature columns
- Coding a simple estimator: Part 1
- Coding a simple estimator: Part 2
- Estimators and neural networks
- Coding a DNN estimator: Part 1
- Coding a DNN estimator: Part 2
- Automating estimator operation
- Estimator automation in practice
6. Deploying Estimators to the Machine Learning Engine
- Creating a GCP project
- Installing the Cloud SDK
- Introduction to Google Cloud Storage
- Accessing Cloud Storage in practice
- Machine Learning Engine
- Deploying jobs to ML Engine