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Course Outline
Introduction
Overview of TensorFlow
- Understanding TensorFlow
- Key features of TensorFlow
Introduction to AI
- Computational Psychology
- Computational Philosophy
Machine Learning
- Theory of computational learning
- Algorithms for computational experiences in computing
Deep Learning
- Artificial neural networks
- Distinctions between deep learning and machine learning
Setting Up the Development Environment
- Installation and configuration of TensorFlow
TensorFlow Quick Start
- Working with nodes
- Utilizing the Keras API
Fraud Detection
- Reading and writing data
- Feature preparation
- Data labeling
- Data normalization
- Dividing data into training and testing sets
- Formatting input images
Predictions and Regressions
- Loading a model
- Visualizing predictions
- Creating regressions
Classifications
- Building and compiling a classifier model
- Training and testing the model
Summary and Conclusion
Requirements
- Experience with Python programming
Target Audience
- Data Scientists
14 Hours
Testimonials (2)
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
Examples/exercices perfectly adapted to our domain
