Improve your skills and acquire new competences on Smoothing Terms or Image Transformations, thanks to the best online university in the world according to Forbes, thanks to TECH"

The process of training deep neural networks can be costly in terms of time and computational resources. However, it is a powerful tool for solving complex machine learning problems and has proven its effectiveness in fields such as image recognition and text generation.

For this reason, TECH has designed a Postgraduate certificate in Training of Deep Neural Networks in Deep Learning with which it seeks to provide students with the necessary skills and competencies to be able to perform their work as experts, with the highest possible efficiency and quality. In this way, throughout this program, aspects such as Maximum Entropy Regularization, Deep Learning or Practical Guidelines will be addressed.

All this, through a convenient 100% online modality that allows students to organize their schedules and studies as best suits them, balancing them with their other day-to-day work and interests. In addition, this program 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 efficiently.

Become an expert in Deep Neural Networks in only 6 weeks and with total freedom of organization"  

This Postgraduate certificate in Training of Deep Neural Networks in Deep Learning contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of practical cases presented by experts in Training of Deep Neural Networks in Deep Learning 
• The graphic, schematic and eminently practical contents of the course provide sportive 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 
• Content that is accessible from any fixed or portable device with an Internet connection 

Enhance your professional profile in one of the most promising areas in the field of Computer Science, thanks to TECH and the most complete materials on the market"

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational 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.

Delve into Feature Extraction and Assessment Metrics, from the comfort of your home and at any time of the day"

Access all Learning Transfer Training content from your tablet, mobile or computer"

The structure and all the teaching resources of this syllabus have been designed by the renowned professionals that make up TECH's team of experts in the area of Deep Learning. These specialists have used their extensive experience and their most advanced knowledge to create practical and completely innovative contents. All this, based on the most efficient pedagogical methodology, TECH's Relearning.

Reach your full potential in the field of Computer Science thanks to the most complete pedagogical and practical materials on the educational market” 

Module 1. Training of Deep Neural Networks

1.1. Gradient Problems

1.1.1. Gradient Optimization Techniques
1.1.2. Stochastic Gradients
1.1.3. Weight Initialization Techniques

1.2. Reuse of Pre-Trained Layers

1.2.1. Learning Transfer Training
1.2.2. Feature Extraction
1.2.3. Deep Learning

1.3. Optimizers

1.3.1. Stochastic Gradient Descent Optimizers
1.3.2. Adam and RMSprop Optimizers
1.3.3. Moment Optimizers

1.4. Learning Rate Programming

1.4.1. Automatic Learning Rate Control
1.4.2. Learning Cycles
1.4.3. Smoothing Terms

1.5. Overfitting

1.5.1. Cross Validation
1.5.2. Regularization
1.5.3. Evaluation Metrics

1.6. Practical Guidelines

1.6.1. Model Design
1.6.2. Selection of Metrics and Evaluation Parameters
1.6.3. Hypothesis Testing

1.7. Transfer Learning

1.7.1. Learning Transfer Training
1.7.2. Feature Extraction
1.7.3. Deep Learning

1.8. Data Augmentation

1.8.1. Image Transformations
1.8.2. Synthetic Data Generation
1.8.3. Text Transformation

1.9. Practical Application of Transfer Learning

1.9.1. Learning Transfer Training
1.9.2. Feature Extraction
1.9.3. Deep Learning

1.10. Regularization

1.10.1. L1 and L2
1.10.2. Regularization by Maximum Entropy
1.10.3. Dropout

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