Course Outline
Introduction
- TensorFlow 2.x vs previous versions -- What's new
Setting up Tensoflow 2.x
Overview of TensorFlow 2.x Features and Architecture
How Neural Networks Work
Using TensorFlow 2.x to Create Deep Learning Models
Analyzing Data
Preprocessing Data
Building a Model
Implementing a State-of-the-Art Image Classifier
Training the Model
Training on a GPU vs a TPU
Evaluating the Model
Making Predictions
Evaluating the Predictions
Debugging the Model
Saving a Model
Deploying a Model to the Cloud
Deploying a Model to a Mobile Device
Deploying a Model to an Embedded System (IoT)
Integrating a Model with Different Languages
Troubleshooting
Summary and Conclusion
Requirements
- Programming experience in Python.
- Experience with the Linux command line.
Audience
- Developers
- Data Scientists
Testimonials (3)
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course - TensorFlow Extended (TFX)
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course - TensorFlow for Image Recognition
I liked the opportunities to ask questions and get more in depth explanations of the theory.