Acquire new knowledge about Object Tracking Algorithms and Advantages of Pretrained Models, thanks to the best online university in the world according to Forbes"

Processing sequences are a fundamental technique in Deep Learning that has proven to be very effective in solving problems in different fields. These techniques allow neural networks to understand the temporal or spatial structure of the input data, which improves the accuracy of predictions and the quality of solutions.

For this reason, TECH has designed a Postgraduate certificate in Deep Learning Processing Sequences with which it seeks to provide students with the necessary skills and competencies to be able to perform their work as specialists, with the highest possible efficiency and quality. Therefore, throughout this program, aspects such as Natural Language processing or Generative Models, Principal Component Analysis or Cross Validation will be addressed.

All this, through a convenient 100% online modality that allows students to organize their schedules and studies, combining them with their other 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 quickly and accurately.

Become an expert in Deep Learning in only 6 weeks and with total freedom to organize your schedule, so you can combine your studies with your other occupations”

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 Deep Learning Processing Sequences 
• 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 

Reach your maximum potential as an expert in Deep Learning Processing Sequences, thanks to TECH and the most innovative materials”

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. 

Access all RNN and CNN Practical Application content from your tablet, mobile or computer"

Delve into Partial Sequence Learning and Reinforcement Learning, from the comfort of your home and at any time of the day"

 

All the didactic resources of this program have been designed by the renowned professionals that make up TECH's team of experts in the area of Computer Science. These specialists have used their extensive experience and state-of-the-art knowledge to create practical and completely up-to-date content. All this, based on the most efficient pedagogical methodology,  TECH Relearning.

The most comprehensive and complete view of one of the most important areas of Deep Learning, so you achieve success quickly and accurately" 

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

1.1. Neurons and Recurrent Layers 

1.1.1. Recurrent Neuron Types 
1.1.2. Recurrent Layer Architecture 
1.1.3. Recurrent Layer Applications 

1.2. Training of Recurrent Neural Networks (RNN) 

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

1.3. RNN Model Evaluation 

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

1.4. Pretrained RNNs 

1.4.1. Pretrained Networks 
1.4.2. Learning Transfer 
1.4.3. Fine Tuning 

1.5. Forecasting a Time Series 

1.5.1. Statistical Forecasting Models 
1.5.2. Time Series Models 
1.5.3. Neural Network Based Models 

1.6. Interpretation of the Results of Time Series Analysis 

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

1.7. Handling Long Sequences 

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

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 Classic Results 

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

Thanks to the most efficient pedagogical methodology, you will be able to acquire new knowledge in an agile and progressive way, without spending too much time studying"