Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Tutorial
Module Offerings
7146COMP-JAN-CTY
Aims
To develop knowledge of advanced topics in deep learning at masters level and provide guidance on the purpose, design and development of deep learning projects using advanced constructs.
To provide an understanding of the range of tools, techniques and algorithms used in advanced deep learning architectures.
To provide help on establishing advanced deep learning design and development principles to successfully complete a deep learning project.
Learning Outcomes
1.
Demonstrate a deep, systemic understanding of the theoretical principles and objectives of advanced Deep Learning (DL) principles
2.
Critically evaluate and determine the applicability of a range of advanced DL concepts and techniques.
3.
Select advanced DL algorithms and architectures to solve particular tasks
4.
Implement and test different advanced DL algorithms and architectures using a suitable language, e.g. Python and associated frameworks
5.
Evaluate advanced DL algorithms and architectures to determine their strengths and weaknesses
6.
Critically evaluate the merits of advanced DL techniques and utilise them appropriately.
Module Content
Outline Syllabus:GPU-enabled Machine Learning
2. Convolutional Neural Networks – Part 1
3. Convolutional Neural Networks – Part 2
4.Transfer Learning Concepts and Approaches
5. Object Detection
6. Object Segmentation
7. Long-Term Short-Term Deep Neural Networks
8. One Dimensional Convolutional Neural Networks
9. Time Series Deep Learning
10. Natural Language Processing with Deep Learning
11. Real-World Applications of Deep Learning; Future Directions in Deep Learning
Module Overview:
This module provides advanced skills required in deep learning to conduct a wide variety of projects in signal processing, object detection, natural language processing and time series analysis. These skills will help to equip you with advanced skills in deep learning. They are practical core requirements for a successful career as a deep learning engineer in industry.
This module provides advanced skills required in deep learning to conduct a wide variety of projects in signal processing, object detection, natural language processing and time series analysis. These skills will help to equip you with advanced skills in deep learning. They are practical core requirements for a successful career as a deep learning engineer in industry.
Additional Information:This module provides advanced skills required in deep learning to conduct a wide variety of projects in signal processing, object detection, natural language processing and time series analysis. These skills will help to equip the student with advanced skills in deep learning. These skills will be practical core requirements for a successful career as a deep learning engineer in industry.