Specialize in Transfer Learning to raise your professional horizons and make the leap to the powerful computer industry, through this 100% online Postgraduate certificate" 

Deep Computer Vision is one of the most important areas of Deep Learning, because it focuses on training Neural Network models to both understand and analyze images in an automated way. Among its main benefits is the automatic extraction of relevant features from visual resources during the architecture training process. In this way, it eliminates the need to manually design algorithms to extract the information.In turn, it simplifies this process while reducing aspects such as the time or effort required. In addition, this system has a wide range of applications such as the identification and tracking of human faces in videos. This is especially useful in biometric in biometric security procedures. 

In this scenario, TECH is launching a state-of-the-art Postgraduate certificate in Deep Computer Vision with Convolutional Neural Networks. The program will allow students to renew their knowledge, while acquiring new skills in image processing and Deep Learning.  The curriculum will delve into essential issues such as the functions of the visual cortex, the reuse of weights in convolution or the principles of activation. The syllabus will also provide students with the keys to implement grouping layers with the Keras tool. This will enable graduates to appropriately reduce the dimensionality of features previously extracted by convolution layers. Also the didactic materials will highlight the importance of pre-rendered models for Transfer Learning, as well as the localization of objects in images and their corresponding tracking by means of algorithms or tracking techniques.

To reinforce these contents, the methodology of this program reinforces its innovative character. TECH offers a 100% online learning environment, adapted to the needs of busy professionals. It also uses the Relearning methodology, based on the repetition of key concepts to fix knowledge and facilitate learning. In this way, the combination of flexibility and a robust pedagogical approach makes it highly accessible.

You will get the most out of the Keras tool and experiment with a plurality of Neural Network architectures in an efficient way”

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 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

You will expertly handle 2D Convulsion and extract the most important features from an image"

The program’s teaching staff includes professionals from the sector 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.  

You will have the opportunity to consult your doubts directly with the teaching staff, who will provide you with personalized tutoring according to your needs and demands"

A study plan based on the revolutionary Relearning methodology, which will allow you to consolidate complex concepts with efficiency and dynamism"

From a theoretical-practical perspective, this Postgraduate certificate will delve into the essential fundamentals of Convolutional Neural Networks, including both their usefulness and the mathematical principles behind their operation. Designed by experts in Deep Computer Vision, the syllabus will analyze the Cortex Visual Architecture by exploring its main theories and image processing models. Students will delve into the different types of Pooling to achieve a more efficient processing and effective feature extraction in Computer Vision applications. In addition, they will nurture their praxis with the most advanced techniques in object detection and tracking.

You will implement CNNs architectures efficiently and train them for a variety of tasks such as image classification, semantic segmentation or object detection”

Module 1. Deep Computer Vision with Convolutional Neural Networks

1.1. The Visual Cortex 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. Deep Computer Vision Classification and Localization

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

Reach your full potential in the Field of Computer Science thanks to the most complete pedagogical and practical materials on the educational market” Enroll now!”