Advanced Deep Learning is your gateway to mastering one of the most transformative technologies of our time. As industries race to harness the power of artificial intelligence, the demand for deep learning experts is soaring. This course is designed to equip you with cutting-edge skills in both supervised and unsupervised deep learning, which is essential for solving complex real-world problems. Explore sophisticated algorithms and techniques which drive innovation in fields such as healthcare, finance and autonomous systems. By taking part, you will gain the ability to design and implement advanced neural networks, placing yourself at the forefront of Advances in AI. Join this immersive online experience and unlock your potential to shape the future through deep learning.
Advanced deep learning
AI Development
40 hours
Introduction
Objectives
- To master supervised learning techniques using deep neural networks.
- To develop advanced models for complex data sets in supervised learning.
- To explore unsupervised learning methods and their applications.
- To implement clustering and dimensionality reduction in unsupervised learning.
- To analyse data patterns using advanced unsupervised algorithms.
- To optimise neural network architectures for specific tasks.
- To evaluate deep learning models for solving real-world problems.
Table of Contents
UNIT 1. SUPERVISED DEEP LEARNING (I)
Introduction
Review: Artificial neural network (ANN)
Review: ANN exercises
Convolutional Neural Networks (CNN)
CNN Exercises
UNIT 2. SUPERVISED DEEP LEARNING (II)
Natural language processing (I)
Recurrent neural networks (RNN) (I)
Recurrent neural networks (RNN) (II)
Natural language processing (II)
RNN Exercise
UNIT 3. UNSUPERVISED DEEP LEARNING (I)
Boltzmann Machines (BM)
Restricted Boltzmann Machines (RBM)
Recommendation systems
Recommendation systems: metrics
RBM exercise
UNIT 4. UNSUPERVISED DEEP LEARNING (II)
Self-organising maps (SOM)
SOM exercises
Autoencoders (AE)
AE exercises
Suggested exercise