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 skyrocketing. This course is designed to equip you with cutting-edge skills in both supervised and unsupervised deep learning, essential for solving complex real-world problems. Delve into sophisticated algorithms and techniques that drive innovations in fields such as healthcare, finance, and autonomous systems. By participating, you will gain the ability to design and implement advanced neural networks, positioning yourself at the forefront of AI advancements. Join this immersive online experience and unlock your potential to impact the future with deep learning.
Advanced deep learning
Desarrollo de IA
40 h
Presentación
Objetivos
- 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 real-world problem-solving.
Índice de contenidos
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)
Recommender systems
Recommender systems: metrics
RBM exercise
UNIT 4. UNSUPERVISED DEEP LEARNING (II)
Self-organizing maps (SOM)
SOM exercises
Autoencoders (AE)
AE exercises
Proposed exercise