You will master the main types of CNN layers and identify larger portions of the images thanks to this 100% online program"

Convolutional Networks have established themselves as a versatile tool in the field of Computer Vision. Its importance lies in its ability to analyze, understand and process images or videos in an automated and efficient way. Among the diversity of its applications, it stands out its relevance in Biomedical Authentication by analyzing unique facial characteristics of a person and comparing them with a database to verify their identity. This is indispensable in aspects such as airport security or access control in buildings, among others.  

In this context, TECH is developing a postgraduate diploma that will comprehensively address Deep Learning Applied to Computer Vision. The curriculum will delve into the use of Machine learning, given its importance to recognize patterns and perform specific analysis tasks. Likewise, the syllabus will address the whole cycle of creation of a Neural Network, paying careful attention to its learning and validation. On the other hand, students will learn the most advanced strategies for Object Detection and Tracking. In line with this, they will implement cutting-edge evaluation metrics, including the Intersection Over Union or Confidence Score. 

On the other hand, to strengthen the mastery of the contents, this university program applies the revolutionary Relearning system. TECH is a pioneer in the use of this teaching model, which promotes the assimilation of complex concepts through their natural and progressive reiteration. In this way, students do not have to resort to complex techniques such as traditional memorization. In this line, the program also uses materials in various formats such as infographics, interactive summaries or explanatory videos. All this in a convenient 100% online mode, which allows students to adjust their schedules according to their responsibilities and personal circumstances. 

Delve deeper into the Evaluation Metrics of Tracking Algorithms thanks to TECH, the world's best digital university according to Forbes"  

This postgraduate diploma in Deep Learning Applied to Computer Vision contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Deep Learning, computer science and computer vision
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out 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

Do you want to become a Machine Learning expert? Achieve it in only 6 months with this innovative program"  

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

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

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

Update your knowledge in Object Detection through innovative multimedia content"

Forget about memorizing! With the Relearning system you will integrate the concepts in a natural and progressive way"

The main objective of this postgraduate diploma is to provide the students with a comprehensive vision of Deep Learning Applied to Computer Vision. Upon completion of this university program, graduates will have at their disposal the most cutting-edge tools in this field, in order to face their professional practice with the best techniques. In this way, professionals will be able to develop new and exclusive Computer Vision projects based on Deep Learning.  In this way, they will be able to differentiate themselves from their competitors and become authentic references in Machine Learning. 

With the highest rated learning assistance methods in online teaching, this Postgraduate Diploma will allow you to learn smoothly, steadily and effectively”

General Objectives

• Generate specialized knowledge about Deep Learning
• Introduce neural networks and examine how they work
• Analyze metrics for proper learning
• Understanding the mathematics behind neural networks
• Develop convolutional neural networks
• Analyze existing metrics and tools
• Examine the pipeline of an image classification network
• Propose inference methods
• Generate specialized knowledge about object detection neural networks and their metrics
• Identify the different architectures
• Establish use cases
• Examine tracking algorithms and their metrics

Specific Objectives

Module 1. Deep Learning

• Analyze the families that make up the artificial intelligence world
• Compile the main Frameworks of Deep Learning
• Define neural networks
• Present the learning methods of neural networks
• Fundamentals of cost functions
• Establish the most important activation functions
• Examine regularization and normalization techniques
• Develop optimization methods
• Introduce initialization methods

Module 2. Convolutional Neural Networks and Image Classification

• Generate specialized knowledge on convolutional neural networks
• Establish evaluation metrics
• Analyze the performance of CNNs for image classification
• Evaluate Data Augmentation
• Propose techniques to avoid Overfitting
• Examine different architectures
• Compile inference methods

Module 3. Object Detection

• Analyze how object detection networks work
• Examine traditional methods
• Determine evaluation metrics
• Identify the main datasets used in the marketplace
• Propose architectures of the Two Stage Object Detector type
• Analyze Fine Tuning Methods
• Examine different Single Shoot type architectures
• Establish object tracking algorithms
• Apply detection and tracking of people

TECH adapts to your schedule, that's why it has designed a flexible and 100% online program"