Acquire new knowledge about Object Tracking Algorithms and Advantages of Pretrained Models, thanks to the best online university in the world according to Forbes”

The importance of Deep Computer Vision with Convolutional Neural Networks lies in its ability to perform a wide variety of tasks in different fields. These techniques have revolutionized computer vision and have enabled significant advances in fields such as medicine, robotics, security, transportation and industry.

For this reason, TECH has designed a Postgraduate certificate in Deep Computer Vision with Convolutional Neural Networks with which it seeks to provide students with the necessary skills and competencies to be able to perform their work as specialists, with the highest possible efficiency and quality. Thus, throughout this program, aspects such as the Definition of the Input Layer, the Initialization of Weights or the VGG Architecture will be addressed. 

All this, through a convenient 100% online mode that allows students to organize their schedules and studies, combining them with their other work and interests of the day. In addition, this degree has the most complete theoretical and practical materials on the market, which facilitates the student's study process and allows them to achieve their goals quickly and efficiently. 

Become an expert in Deep Computer Vision in only 6 weeks and with total freedom of organization”

This Postgraduate certificate in Deep Computer Vision with Convolutional Neural Networks 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 Computer Vision with Convolutional Neural Networks
• The graphic, schematic and practical contents of the program provide Sports and practical information on those 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

Enhance your professional profile in one of the most promising areas in the IT field, thanks to TECH and the most innovative multimedia materials"

The program’s teaching staff includes professionals from 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 provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive education programmed to learn in real situations.

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

Learn how in use the 2D convolution Application from the comfort of your home at any time Moments the day"

Access all the content on Object Tracking Algorithms from your Tablet, mobile or computer and with total freedom to organize your studies"

The structure and all the didactic resources of this study plan have been designed by the renowned professionals that make up TECH's team of experts in the area of Computer Science. These specialists have used their extensive experience and their most advanced knowledge to create practical and completely updated contents. All this, based on the most efficient teaching methodology, TECH's Relearning. 

Deep Computer Vision's more comprehensive and up-to-date vision will give you the skills you need to succeed in this area”

Module 1. Deep Computer Vision with Convolutional Neural Networks

1.1. The Cortex Visual Architecture

1.1.1. Functions of the Visual Cortex
1.1.2. Theories of computational vision
1.1.3. Models of image processing

1.2. Convolutional layers

1.2.1. Reuse of weights in convolution
1.2.2. 2D convolution
1.2.3. Activation Functions

1.3. Grouping layers and implementation of grouping layers with Keras

1.3.1. Pooling and Striding
1.3.2. Flattening
1.3.3. Types of Pooling

1.4. CNN Architecture

1.4.1. VGG Architecture
1.4.2. AlexNet architecture
1.4.3. ResNet Architecture

1.5. Implementation of a ResNet-34 CNN using Keras

1.5.1. Weight initialization
1.5.2. Input layer definition
1.5.3. Output definition

1.6. Use of pre-trained Keras models

1.6.1. Characteristics of pre-trained models
1.6.2. Uses of pre-trained models
1.6.3. Advantages of pre-trained models

1.7. Pre-trained models for transfer learning

1.7.1. Transfer learning
1.7.2. Transfer learning process
1.7.3. Advantages of transfer learning

1.8. Classification and Localization in Deep Computer Vision

1.8.1. Image Classification
1.8.2. Localization of objects in images
1.8.3. Object Detection

1.9. Object detection and object tracking

1.9.1. Object detection methods
1.9.2. Object tracking algorithms
1.9.3. Tracking and localization techniques

1.10. Semantic Segmentation

1.10.1. Deep learning for semantic segmentation
1.10.2. Edge Detection
1.10.3. Rule-based segmentation methods

Thanks to the most efficient pedagogical methodology, you will be able to acquire new knowledge in a precise way and in only 150 hours”