You will be able to download all the content to any electronic device from the Virtual Campus and consult it whenever you need it, even without an Internet connection” 

The impact of Deep Learning on improving the efficiency and accuracy of systems is undeniable, and is being reflected in a wide variety of fields, from medicine to transportation to security. The applications are numerous, including computer-aided medical diagnosis, autonomous vehicle driving, security system anomaly detection and product supply chain optimization. As new Deep Learning techniques continue to be researched and developed, a wide range of possibilities in complex problem solving and real-time decision making are opening up. 

As a result, the demand for professionals who know how to apply Deep Learning continues to increase, and the trend is expected to continue in the future. To summarize, studying Deep Learning applications can be a solid option due to its growing demand in various industries, its ability to improve the efficiency and accuracy of systems, its wide variety of applications, the resources and support communities available, and the employment opportunities and competitive salaries in the field. 

This program designed by TECH is based on the Relearning methodology to facilitate the student's learning through the progressive and natural repetition of the fundamental concepts. This way, the graduate will acquire the necessary competencies by adjusting the study to their life style. In addition, the completely online format will allow the professional to focus on their learning, without the need to travel or adjust to a fixed schedule, and to access the theoretical and practical content from anywhere and at any time using a device with an Internet connection. 

Take advantage of the unique opportunity for professional and personal growth offered exclusively by this TECH postgraduate diploma” 

This postgraduate diploma in Deep Learning Applications 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 
• 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 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 work
• Content that is accessible from any fixed or portable device with an Internet connection

A postgraduate diploma that provides you with resources and strategies for you to implement PCA techniques with an automatic linear encoder effectively and, in addition, 100% online!”

The program includes in its teaching staff professionals from the sector who bring to this program the experience of their work, as well as recognized 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. This will be done with the help of an innovative system of interactive videos made by renowned experts. 

Enroll now and you will be able to generate texts using recurrent neural networks thanks to the skills you will acquire with this postgraduate diploma"

You will have at your disposal a Virtual Campus available 24 hours a day, without the usual pressure of adapting to rigid academic calendars or unchangeable class schedules"

Through the syllabus, the graduate will acquire knowledge that will allow them to obtain a broad and up-to-date perspective on the key aspects of Deep Learning Applications, which will enable them to achieve the established objectives. As a result, the student will develop comprehensive skills in an essential, versatile and constantly evolving area of engineering, which will lead to excellence in a constantly growing sector. In order to ensure student satisfaction, TECH has established general and specific objectives that will guide the student towards success. 

Upgrade your skills in the data preparation for RNN training using the most innovative theoretical and practical tools available in the online academic market” 

General Objectives

• Lay the foundation for the key concepts of mathematical functions and their derivatives
• Apply these principles to deep learning algorithms to learn automatically 
• Examine the key concepts of Supervised Learning and how they apply to neural network models 
• Analyze the training, evaluation, and analysis of neural network models 
• Lay the foundation for the key concepts and main applications of deep learning 
• Implement and optimizes neural networks with Keras 
• Develop expertise in the training of deep neural networks 
• Analyze the optimization and regularization mechanisms necessary for deep network training 

Specific Objectives

Module 1. Processing Sequences using RNNs and CNNs 

• Analyze the architecture of recurrent neurons and recurrent layers
• Examine the various training algorithms for training RNN models 
• Evaluate the performance of RNN models using accuracy and sensitivity metrics

Module 2. NLP Natural Language Processing with RNN and Attention 

• Generate text using recurrent neural networks 
• Train an encoder-decoder network to perform neural machine translation 
• Develop a practical application of Natural Language Processing with RNN and Attention 

Module 3. Autoencoders, GANs, and Diffusion Models 

• Implement PCA techniques with a linear incomplete automatic encoder 
• Use convolutional and variational autoencoders to improve the results of autoencoders 
• Analyze how GANs and diffusion models can generate new and realistic images  

Delve into the practical applications of natural language processing with RNN and prepare yourself for a wide range of career opportunities in different industries”