Improve your Natural Language Processing skills with RNN, thanks to TECH Global University" 

In the field of artificial intelligence, natural language processing with RNN is used to build chatbots, content recommendation systems and virtual assistants that can understand and respond to user requests in natural language. In addition, training in natural language processing with recurrent neural networks is essential for developing skills in a field of growing demand. 

For this reason, TECH has designed a Postgraduate certificate in Natural Language Processing NLP with RNN in order to provide students with the necessary skills and competencies to be able to work as specialists with the highest possible efficiency and quality. Therefore, throughout this program, topics such as Text Generation Using RNN, Data Cleaning and Transformation or Care Mechanisms are discussed in depth. 

All this, through a convenient 100% online modality that allows students to organize their schedules and studies, combining them with their other day-to-day work. 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 objectives in an agile and precise manner. 

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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 case studies presented by experts in Natural Language Processing NLP with RNN 
• The graphic, schematic and eminently practical contents of the book provide sporting 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 
• The availability of access to content from any fixed or portable device with an Internet connection 

Access all Deep Learning content from your tablet, mobile or computer at any time of the day" 

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.   

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

Delve into aspects such as Image Data Preprocessing from the comfort of your home at any time of the day"

vprofessionals that make up TECH's team of experts in Deep Learning. These specialists have used their extensive experience and the most up-to-date knowledge to create practical and completely innovative content. All of this, based on the most efficient and precise pedagogical methodology, TECH Relearning. 

The most complete and up-to-date view of Natural Language Processing with RNN is found in this unique curriculum"

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

1.1. Text Generation Using RNN 

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

1.2 Creation of the Training Dataset 

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

1.3 Sentiment Analysis 

1.3.1 Classification of RNN Opinions 
1.3.2 Detection of issues in comments 
1.3.3 Sentiment Analysis with Deep Learning Algorithms 

1.4 Encoder-Decoder Network for Neural Machine Translation 

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

1.5 Attention Mechanisms 

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

1.6 Transformers Models 

1.6.1 Use of Transformer Models for Natural Language Processing 
1.6.2 Application of Transformers for Vision Models 
1.6.3 Advantages of Transformer Models 

1.7 Transformers for Vision 

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

1.8 Hugging Face Transformers Bookstore 

1.8.1 Using the Hugging Face Transformers Bookstore 
1.8.2 Application of the Hugging Face Transformer Bookstore 
1.8.3 Advantages of the Hugging Face Transformers Bookstore 

1.9 Other Transformers Bookstores Comparison 

1.9.1 Comparison Between the Different Transformers Bookstores 
1.9.2 Use of the Other Transformers Libraries 
1.9.3 Advantages of the Other Transformers Bookstores 

1.10 Development of a NLP Application with RNN and Care Practical Application 

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

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