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Neural Networks
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: 16506
Transcript Source: Partner Institution
Course Details: Level 300
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: The students are expected to have basic knowledge of
- Calculus
- Programming skills
- Numerical optimization
- Statistical Learning
- Machine Learning
DESCRIPTION
This course introduces the student a set of basic tools to solve learning problems with neural networks. The course provides basic schemas for modeling
of problems of different nature, as well as tools for the numerical optimization of the model based on existing data.
PART I: GENERAL CONCEPTS
1. Introduction to neural networks. The multilayer perceptron.
2. Training of a neural network. Calculation of gradients using reverse propagation.
PART II: SUPERVISED LEARNING
3. Supervised learning with deep neural networks. An introductory example.
4. Sequential modeling: recursive neural networks. An introductory example.
PART III: LEARNING NOT SUPERVISED
5. Non-Supervised Learning with deep neural networks. An introductory example.
6. Generative models using deep neural networks.
of problems of different nature, as well as tools for the numerical optimization of the model based on existing data.
PART I: GENERAL CONCEPTS
1. Introduction to neural networks. The multilayer perceptron.
2. Training of a neural network. Calculation of gradients using reverse propagation.
PART II: SUPERVISED LEARNING
3. Supervised learning with deep neural networks. An introductory example.
4. Sequential modeling: recursive neural networks. An introductory example.
PART III: LEARNING NOT SUPERVISED
5. Non-Supervised Learning with deep neural networks. An introductory example.
6. Generative models using deep neural networks.
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