You will have the guarantee of specializing at an international level in a booming sector that will catapult you to professional success" 

A city is an urban space with a high population density in which diverse activities are developed for social life with a high level of interaction. Consequently, a Smart City provides a traditional city with tools and systems that make its habitability more efficient. This qualification teaches the foundations of the technological architecture of Smart Cities, such as the parameterization and sensorization of their environments, the datification of public infrastructures, the measurement and scanning of social events and the advanced analysis of urban dynamics, among others.

At the same time, there is Natural Language Processing (NLP), understood as the study of how machines understand human language. Nowadays, NLP is used for various functions such as mail filters, wizards, search results, automatic translations, sentiment analysis, automatic subtitles, etc. This Postgraduate diploma provides specialized knowledge about the main current uses of NLP and the libraries that allow working with NLP focused on industrial projects.

Training a model from scratch requires a large amount of previously catalogued information, approximately 10,000 photos of each of the types to be differentiated. This takes hours to achieve good results. But in many cases, it is possible to start from previously trained models, this technique is known as Transfer Learning. This program examines which network models are currently available, in order to facilitate the training of any model by applying the Transfer Learning technique.

Over the course of 6 months, The Student Body will deepen their understanding of the scope of application of each technology, understanding the competitive advantages they bring, so that they will be positioned at the technological forefront and will be able to lead ambitious projects, now and in the future. Furthermore, graduates have the best 100% online study methodology, which eliminates the need to attend classes in person or be constrained by a predetermined schedule.

You will only need a device with Internet access to develop the ability to innovate in the market as an active part of the real digital transformation" 

This Postgraduate diploma in Smart Cities and Artificial Intelligence (AI) contains the most complete and up-to-date educational program on the market. Its most notable features are:

• Case studies presented by experts in Smart Cities and Artificial Intelligence 
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments 
• Content that is accessible from any fixed or portable device with an Internet connection

You will examine which network models are currently available, in order to enhance the training of our model by applying the Transfer Learning technique"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will allow professionals to learn in a contextual and situated learning environment, i.e., a simulated environment that will provide immersive education programmed to prepare in real situations.

The design of this program focuses on Problem-Based Learning, by means of which professionals must try to solve the different professional practice situations that are presented to them throughout the course. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

With the implementation of a Smart City, it collects and returns data in real time, turning into an entity with a life of its own"

Study through Word Embedings the different techniques in which words or phrases are related to each other"

This Postgraduate diploma consists of a 3-module syllabus. The first module focuses on innovation, through Smart Cities, an area of work that is booming and in which there is still a long way to go, much to discover and much to investigate. Finally, modules 2 and 3 address two of the fields with the greatest development forecasts in the world of Artificial Intelligence, NLP and computer vision, which are closely related to robotics, autonomous vehicles and the emerging field of Emotive Computing.   

With the support of a team of experts, you will acquire up-to-date knowledge that will favor your professional growth” 

Module 1. Smart Cities as innovation tools

1.1. From Cities to Smart Cities

1.1.1. From Cities to Smart Cities
1.1.2. Cities Over Time and Cultures in Cities
1.1.3. Evolution of City Models

1.2. Technologies

1.2.1. Technological Application Platforms
1.2.2. Services/Citizen Interfaces
1.2.3. Technological Typologies

1.3. City as a Complex System

1.3.1. Components of a City
1.3.2. Interactions between Components
1.3.3. Applications: Products and Services in the City

1.4. Intelligent Safety Management

1.4.1. Current State
1.4.2. Technological Management Environments in the City
1.4.3. Future: Smart Cities in the Future

1.5. Intelligent Cleaning Management

1.5.1. Application Models in Intelligent Cleaning Services
1.5.2. Systems: Application of Intelligent Cleaning Services
1.5.3. Future of Intelligent Cleaning Services

1.6. Intelligent Traffic Management

1.6.1. Traffic Evolution: Complexity and Factors Hindering Traffic Management
1.6.2. Problems
1.6.3. E-Mobility
1.6.4. Solutions

1.7. Sustainable City

1.7.1. Energy
1.7.2. The Water Cycle
1.7.3. Management Platform

1.8. Intelligent Leisure Management

1.8.1. Business Models
1.8.2. Urban Leisure Evolution
1.8.3. Associated Services

1.9. Large Social Event Management

1.9.1. Movement
1.9.2. Capacities
1.9.3. Health

1.10. Conclusions on the Present and Future of Smart Cities

1.10.1. Technology Platforms and Problems
1.10.2. Technologies, Integration in Heterogeneous Environments
1.10.3. Practical Applications in Different City Models

Module 2. R&D and AI NLP/NLU. Embeddings and Transformers

2.1. Natural Language Processing (NLP)

2.1.1. Natural Language Processing. Uses of NLP
2.1.2. Nautral Language Processing (NLP). Libraries
2.1.3. Stoppers in NLP Application

2.2. Natural Language Understanding/Natural Language Generation. (NLU/NLG)

2.2.1. NLG. I.A. NLP/NLU. Embeddings and Transformers
2.2.2. NLU/NLG. Uses
2.2.3. NLP/NLG. Differences

2.3. Word Embeddings

2.3.1. Word Embeddings
2.3.2. Word Embeddings Uses
2.3.3. Word2vec. Libraries

2.4. Embeddings. Practical Application

2.4.1. word2vec Code
2.4.2. Word2vec. Real Cases
2.4.3. Corpus for Word2vec Use. Examples:

2.5. Transformers

2.5.1. Transformers
2.5.2. Models Created with Transformers
2.5.3. Pros and Cons of Transformers

2.6. Sentiment Analysis

2.6.1. Sentiment Analysis
2.6.2. Practical Application of Sentiment Analysis
2.6.3. Uses of Sentiment Analysis

2.7. GPT Open AI

2.7.1. GPT Open AI
2.7.2. GPT 2. Free Disposal Model
2.7.3. GPT 3. Payment Model

2.8. Hugging Face Community

2.8.1. Hugging Face Community
2.8.2. Hugging Face Community Possibilities
2.8.3. Hugging Face Community Examples:

2.9. Barcelona Super Computing Case

2.9.1. BSC Case
2.9.2. MARIA Model
2.9.3. Existing Corpus
2.9.4. Importance of Having a Large Spanish Language Corpus

2.10. Practical Applications

2.10.1. Automatic Summary
2.10.2. Text Translation
2.10.3. Sentiment Analysis
2.10.4. Speech Recognition

Module 3. R&D and AI Computer Vision. Object Identification and Tracking

3.1. Computer Vision

3.1.1. Computer Vision.
3.1.2. Computational Vision
3.1.3. Interpretation of the Machines in an Image

3.2. Activation Functions

3.2.1. Activation Functions
3.2.2. Sigmoid
3.2.3. RELU
3.2.4. Hyperbolic Tangent
3.2.5. Softmax

3.3. Construction of Convolutional Neural Networks

3.3.1. Convolution Operation
3.3.2. ReLU Layer
3.3.3. Pooling
3.3.4. Flattering
3.3.5. Full Connection

3.4. Convolution Process

3.4.1. How a Convolution Works
3.4.2. Convolution Code
3.4.3. Convolution. Application

3.5. Transformations with Images

3.5.1. Transformations with Images
3.5.2. Advanced Transformations
3.5.3. Transformations with Images. Application
3.5.4. Transformations with Images. Use Case

3.6. Transfer Learning

3.6.1. Transfer Learning
3.6.2. Transfer Learning Typology
3.6.3. Deep Networks to Apply Transfer Learning

3.7. Computer Vision. Use Case

3.7.1. Image Classification
3.7.2. Object Detection
3.7.3. Object Identification
3.7.4. Object Segmentation

3.8. Object Detection

3.8.1. Convolution-Based Detection
3.8.2. R-CNN, Selective Search
3.8.3. Rapid Detection with YOLO
3.8.4. Other Possible Solutions

3.9. GAN. Generative Adversarial Networks

3.9.1. Generative Adversarial Networks
3.9.2. Code for a GAN
3.9.3. GAN. Application

3.10. Application of Computer Vision Models

3.10.1. Content Organization
3.10.2. Visual Search Engines
3.10.3. Facial Recognition
3.10.4. Augmented Reality
3.10.5. Autonomous Driving
3.10.6. Fault Identification at each Assembly
3.10.7. Pest Identification
3.10.8. Health

Build an industrial project through Smart Cities Smart Cities, a booming area of work"