Course Outline
Introduction
- Differences between TensorFlow 2.x and prior iterations -- Key updates
Establishing the TensorFlow 2.x Environment
Functional Capabilities and Structural Framework of TensorFlow 2.x
Operational Principles of Neural Networks
Application of TensorFlow 2.x in Developing Deep Learning Solutions
Data Analysis
Data Preprocessing Protocols
Model Construction
Implementation of an Advanced Image Classification System
Model Training Procedures
Comparative Analysis: GPU and TPU Training Environments
Model Assessment
Execution of Predictive Inference
Validation of Inference Results
Model Diagnostics
Preservation of Model Artifacts
Deployment of Models to Cloud Infrastructure for government applications
Deployment of Models to Mobile Devices
Deployment of Models to Embedded Systems (IoT)
Integration of Models Across Multiple Programming Languages
Troubleshooting Procedures
Summary and Conclusion
Requirements
- Proficiency in Python programming.
- Familiarity with Linux command-line operations.
Intended Audience
- Software Developers
- Data Scientists
Testimonials (4)
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
Trainer's knowledge and the fact they were very approachable. They could easily convey important knowledge
Mateusz Stachyra - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
I liked that we covered the basics too
Tomasz - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.
