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Course Outline
Introduction
TensorFlow Overview
- Understanding TensorFlow
- Key features of TensorFlow
Artificial Intelligence Basics
- Computational Psychology
- Computational Philosophy
Machine Learning Fundamentals
- Computational learning theory
- Algorithms for computational experience
Deep Learning Concepts
- Artificial neural networks
- Differences between deep learning and machine learning
Setting Up the Development Environment
- Installing and configuring TensorFlow
Getting Started with TensorFlow
- Working with nodes
- Utilizing the Keras API
Fraud Detection Techniques
- Data input and output operations
- Feature preparation
- Data labeling
- Data normalization
- Splitting data into training and test sets
- Formatting input images
Predictions and Regression
- Loading pre-trained models
- Visualizing prediction outcomes
- Implementing regression analysis
Classification Methods
- Building and compiling classifier models
- Training and evaluating the model
Summary and Conclusion
Requirements
- Experience with Python programming
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
