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Course Outline
Introduction
Overview of Neural Networks
Understanding Convolutional Networks
Setting up Keras
Overview of Keras Features and Architecture
Overview of Keras Syntax
Understanding How a Keras Model Organize Layers
Configuring the Keras Backend (TensorFlow or Theano)
Implementing an Unsupervised Learning Model
Analyzing Images with a Convolutional Neural Network (CNN)
Preprocessing Data
Training the Model
Training on CPU vs GPU vs TPU
Evaluating the Model
Using a Pre-trained Deep Learning Model
Setting up a Recurrent Neural Network (RNN)
Debugging the Model
Saving the Model
Deploying the Model
Monitoring a Keras Model with TensorBoard
Troubleshooting
Summary and Conclusion
Requirements
- Python Programming experience.
- Experience with the Linux command line.
Audience
- Developers
- Data scientists
21 Hours
Testimonials (5)
We had an overview about Machine Learning, Neural Networks, AI with practical examples.
Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
Very flexible.
