Course Outline
Introduction
- TensorFlow 2.x versus previous versions -- Key innovations
Setting up TensorFlow 2.x
Overview of TensorFlow 2.x Features and Architecture
Understanding Neural Networks
Utilizing TensorFlow 2.x for Deep Learning Model Creation
Data Analysis
Data Preprocessing
Model Construction
Implementing a State-of-the-Art Image Classifier
Model Training
Training on GPU versus TPU
Model Evaluation
Generating Predictions
Evaluating Prediction Outcomes
Model Debugging
Model Storage
Cloud Deployment of Models
Mobile Device Deployment of Models
Deployment to Embedded Systems (IoT)
Integration with Various Programming Languages
Troubleshooting
Summary and Conclusion
Requirements
- Programming proficiency in Python.
- Familiarity with the Linux command line.
Target Audience
- 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.
