Basic principles of artificial intelligence, machine learning and neural networks
TensorFlow: introduction and installation
TensorFlow deployment: models, activation functions, loss functions, layers, initializers, metrics, callbacks, optimizers etc.
Practical TensorFlow examples: regression methods, text and image classification (convolutional neural networks)
TensorBoard: model design, selection and evaluation
- Research and Academia
- Public Sector
Basics of data analysis and coding in Python programming language.