A 100% online Postgraduate certificate that will provide you with the most effective methods to predict future values in a sequence of data that vary over time" 

Deep Learning Processing Sequences are an essential aspect of Deep Learning. The main reasons are that these tools enable effective modeling of sequential data, capture complex temporal dependencies and enable a wide range of applications in numerous fields. In turn, these intelligent systems continuously develop algorithms that drive innovation, enabling advances ranging from personalized medicine to machine translation or even the prediction of astronomical phenomena. In this sense, their ability to learn from large amounts of data and identify complex patterns is generating new opportunities for research. 

Within this framework, TECH is developing a pioneering Postgraduate certificate in Deep Learning Processing Sequences. Aimed at professionals, researchers and entrepreneurs, this program will delve into both the types of Recurrent Neurons and the architecture of the layers. Likewise, the syllabus will provide students with the most advanced techniques for the training of Recurrent Neural Networks (highlighting Backpropagation  over time). In this regard, the curriculum will delve into the most effective Evaluation Metrics to guarantee the performance of Deep Learning models. On the other hand, the program will include multiple practical applications of Convolutional Neural Networks, such as computer vision, pattern recognition or natural language processing.

To facilitate the assimilation of all these contents, TECH offers first class pedagogical tools, to which students will have access 24 hours a day. The only thing they will need to enter the Virtual Campus is an electronic device with an Internet connection, serving their own smartphone, Tabletor computer. At the same time, throughout their learning process they will have the support of a teaching staff specialized in Computer Vision. These professionals will resolve any doubts that students may have, in addition to providing them with personalized advice to ensure that they experience a quality leap in their professional career. 

Do you want to optimize your management of Gated Recurrent Units? Master this Neural Network architecture in only 6 weeks with this program"

This Postgraduate certificate in Deep Learning Processing Sequences 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 Processing Deep Learning Sequences
• 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

An intensive university program that will increase your performance level and skills to carry out Learning Transfer efficiently"

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.  

By studying this intensive program you will increase your performance level and teaching skills"

Thanks to the Relearning learning method, you will not invest long hours of study to memorize. You will learn in a progressive and natural way!"

The didactic materials that make up this academic itinerary are designed to provide students with a comprehensive approach to Processing Sequences in Deep Learning. To achieve this, the program will delve into essential concepts such as neurons, recurrent layers and model training. In addition, students will examine the most advanced tools for Evaluation Metrics, including cross-validation and hyperparameter tuning. As such, graduates will incorporate these tools into their practice to measure and understand the performance of models in a variety of tasks such as transforming text into audio signal sequences.

You will examine the latest scientific evidence regarding Recurrent Neural Network Training to enrich your regular praxis”

Module 1. Processing Sequences using RNN (Recurrent Neural Networks) and CNN (Convolutional Neural Networks)

1.1. Recurrent Neurons and Layers

1.1.1. Types of Recurring Neurons
1.1.2. Architecture of a Recurrent Layer
1.1.3. Applications of Recurrent Layers

1.2. Recurrent Neural Network (RNN) Training

1.2.1. Backpropagation over Time (BPTT)
1.2.2. Stochastic Downward Gradient
1.2.3. Regularization in RNN Training

1.3. Evaluation of RNN Models

1.3.1. Evaluation Metrics
1.3.2. Cross Validation
1.3.3. Hyperparameter Tuning

1.4. Prerenal RNNs

1.4.1. Prenetrated Networks
1.4.2. Transfer of Learning
1.4.3. Fine Tuning

1.5. Forecasting a Time Series

1.5.1. Statistical Models for Forecasting
1.5.2. Time Series Models
1.5.3. Models based on Neural Networks

1.6. Interpretation of Time Series Analysis Results

1.6.1. Main Component Analysis
1.6.2. Cluster Analysis
1.6.3. Correlation Analysis

1.7. Handling of Long Sequences

1.7.1. Long Short-Term Memory (LSTM)
1.7.2. Gated Recurrent Units (GRU)
1.7.3. 1D Convolutionals

1.8. Partial Sequence Learning

1.8.1. Deep Learning Methods
1.8.2. Generative Models
1.8.3. Reinforcement Learning

1.9. Practical Application of RNN and CNN

1.9.1. Natural Language Processing
1.9.2. Pattern Recognition
1.9.3. Computer Vision

1.10. Differences in Classical Results

1.10.1. Classical vs. RNN Methods
1.10.2. Classical vs. CNN Methods
1.10.3. Difference in Training Time

You will have a library of top-quality multimedia resources that will enhance your knowledge about Deep Learning"