You will analyze consumer sentiment through intelligent algorithms, with the maximum scientific rigor of TECH: an institution at the forefront of technology"

A new study by prestigious researchers forecasts that the Natural Networks market will grow by 21.5% over the next few years, reaching an investment of 1.02 billion dollars. This reflects the relevance that this aspect of Deep Learning is acquiring in society. At this juncture, the development of neural architectures has enabled computers to learn to make informed decisions without human intervention, which opens up a wide range of applications. For example, Recurrent Neural Networks are used to develop virtual assistants or chatbots, which interact with users in natural language to provide the assistance they need.

In view of this reality, TECH is launching a Postgraduate certificate in NLP Natural Language Processing with RNN. This program will provide students with the skills required to work as specialists in this field, with the highest parameters of efficiency and quality. Therefore, the syllabus will delve into the creation of the training dataset, as well as its proper storage. In this sense, the syllabus will analyze different applications of neural structures, such as the Encoder-Decoder Network for machine translation. In turn, the didactic materials will examine the handling of Transformer Models so that graduates will be able to capture complex relationships in data sequences. 

It should be noted that to the methodology of the program is supportedby the revolutionary Relearning teaching system. TECH is a pioneer in this learning model, based on the reiteration of content so that students enjoy expanding their knowledge and skills in a natural, flexible and progressive way. The only thing students will need is an electronic device capable of connecting to the Internet, in order to enter the Virtual Campus and access the most dynamic academic resources on the market.

Do you want to optimize your practical programming skills? With this course you will handle the most innovative Deep Learning libraries"  

This Postgraduate certificate in Natural Language Processing NLP with RNN contains the most complete and up-to-date program on the market. The most important features include:

• The development of practical cases presented by experts in Natural Language Processing NLP with RNN
• 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 delve into the use of Attention Mechanisms, to improve the accuracy and robustness of your models"

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 control the Encoder-Decoder to incorporate neural machine translation into your projects"

TECH is a pioneer in the Relearning methodology, which will provide you with a flexible and progressive learning experience"

Developed by Deep Learning professionals, this curriculum will give graduates a thorough understanding of Natural Language Processing with Recurrent Neural Networks. With a practical approach, didactic content will focus on factors such as text generation, model training or data set creation at that stage. Also, the syllabus will address the importance of the Cleaning and Transformation of information, to improve the effectiveness of the resulting systems. The course will analyze Deep Learning Attention Mechanisms, allowing students to manage long sequences and capture long-term relationships. 

You will master the techniques of Processing Natural Language in a variety of practical applications such as extraction information and answer questions" 

Module 1. Natural Language Processing (NLP) with Natural Recurrent Networks (NRN) and Attention

1.1. Text Generation Using RNN

1.1.1. Training an RNN for Text Generation
1.1.2. Natural Language Generation with RNN
1.1.3. Text Generation Applications with RNN

1.2. Training Data Set Creation

1.2.1. Preparation of the Data for Training an RNN
1.2.2. Storage of the Training Dataset
1.2.3. Data Cleaning and Transformation

1.3. Sentiment Analysis

1.3.1. Classification of Opinions with RNN
1.3.2. Detection of Themes in Comments
1.3.3. Sentiment Analysis with Deep Learning Algorithms

1.4. Encoder-decoder Network for Neural Machine Translation

1.4.1. Training an RNN for Machine Translation
1.4.2. Use of an Encoder-decoder Network for Machine Translation
1.4.3. Improving the Accuracy of Machine Translation with RNNs

1.5. Attention Mechanisms

1.5.1. Application of Care Mechanisms in RNN
1.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
1.5.3. Advantages of Attention Mechanisms in Neural Networks

1.6. Transformer Models

1.6.1. Using TransformerModels for Natural Language Processing
1.6.2. Application of Transformer Models for Vision
1.6.3. Advantages of Transformers Models

1.7. Transformers for Vision

1.7.1. Use of Transformer Models for Vision
1.7.2. Image Data Preprocessing
1.7.3. Training a Transformers Model for Vision

1.8. Hugging Face Transformer Library

1.8.1. Using the Hugging Face Transformers Library
1.8.2. Application of the Hugging Face Transformers Library
1.8.3. Advantages of the Hugging Face Transformers library

1.9. Other Transformers Libraries. Comparison

1.9.1. Comparison between different TransformersLibraries
1.9.2. Use of the other Transformers Libraries
1.9.3. Advantages of the other Transformers Libraries

1.10. Development of an NLP Application with RNN and Attention. Practical Application

1.10.1. Development of a Natural Language Processing Application with RNN and Attention
1.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
1.10.3. Evaluation of the Practical Application

Are you looking for a university program that can be harmonized with your daily obligations? You are facing the right program, because TECH adapts to your circumstances"