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