The course in Advanced Deep Learning It is your gateway to one of the most dynamic and in-demand fields in the world of technology. With the unstoppable advance of artificial intelligence, deep learning has become an essential tool for transforming industries and solving complex problems. On this course, you will acquire specialist skills in neural networks, recommendation systems and learning strategies, using modern tools such as Python, Keras and TensorFlow. This course, designed to suit your needs through distance learning, will give you the flexibility to learn from anywhere, preparing you to lead the next generation of artificial intelligence technologies.
Sector-specific
AI Development
40 hours
Advanced Deep Learning
AI Development
40 hours
Introduction
Objectives
• Understand the differences between machine learning and deep learning.
• Identify and apply clustering algorithms to extract data structures.
• Develop recommendation systems using collaborative and hybrid filtering.
• Apply classifiers and algorithms to improve accuracy in classification tasks.
• Design and train neural networks using Python, Keras and TensorFlow.
• Implement multi-layer networks and understand how they work, using practical examples.
• Analyse and apply learning strategies to optimise deep neural networks.
Table of Contents
TEACHING UNIT 1. INTRODUCTION TO MACHINE LEARNING
Introduction
Classification of machine learning algorithms
Examples of machine learning
Differences between machine learning and deep learning
Types of machine learning algorithms
The future of machine learning
TEACHING UNIT 2. EXTRACTING DATA STRUCTURE: CLUSTERING
Introduction
Algorithms
TEACHING UNIT 3. RECOMMENDATION SYSTEMS
Introduction
Collaborative filtering
Clustering
Hybrid recommendation systems
TEACHING UNIT 4. CLASSIFICATION
Filing systems
Algorithms
TEACHING UNIT 5. NEURAL NETWORKS AND DEEP LEARNING
Components
Learning
TEACHING UNIT 6. ELECTION SYSTEMS
Introduction
The process of transitioning from DSS to IDSS
Case studies
TEACHING UNIT 7. DEEP LEARNING WITH PYTHON, KERAS AND TENSORFLOW
Deep learning
Deep Learning Environment with Python
Machine learning and deep learning
TEACHING UNIT 8. NEURAL SYSTEMS
Neural networks
Deep networks and shallow networks
TEACHING UNIT 9. SINGLE-LAYER NETWORKS
Single-layer and multi-layer perceptrons
Example of a perceptron
TEACHING UNIT 10. MULTILAYER NETWORKS
Types of deep networks
TEACHING UNIT 11. LEARNING STRATEGIES
Data input and output
Training a neural network
Computer graphics
Implementation of a deep neural network
The direct propagation algorithm
Multilayer deep neural networks
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