You will have at your disposal a Virtual Campus available 24 hours a day, without adapting to fixed schedules or imposed schedules that do not suit you” 

Neural Networks in Deep Learning are an essential tool for data processing and complex problem solving in Engineering. Their ability to learn and adapt to different situations makes them ideal for tasks such as pattern recognition, data classification and real-time decision making. In addition, their use in fields such as computer vision and natural language processing has led to important advances in technology, such as facial recognition and machine translation. 

Given this reality, this TECH academic program is designed as a response to the growing demand for highly qualified professionals in this area. This program has been specifically designed to provide solid training in the use of Neural Networks in Deep Learning, with a focus on their practical application in different fields of Engineering. Students will have the opportunity to learn state-of-the-art tools such as Tensorflow and Keras, and will acquire the necessary skills to design, implement and optimize Neural Network models to solve real-world problems. 

For this reason, this complete program designed by TECH is based on the Relearning methodology to facilitate the student's learning through the progressive and natural repetition of the fundamental concepts. In this way, the graduate will acquire the necessary skills by adjusting the study to their life style. In addition, the online format will allow the professional to access the theoretical-practical contents anywhere and at any time, without the need to travel or adjust to a fixed schedule. Moreover, you will be able to access the theoretical and practical contents at any time and place, as long as you have a device with an Internet connection. 

TECH seeks to project your professional career without neglecting other areas of your life, that's why it offers you a flexible and adaptable teaching to your needs” 

This Postgraduate certificate in Neural Networks in Deep Learning contains the most complete and up-to-date program on the market. Its most outstanding features are:

• The development of case studies presented by experts in Deep Learning 
• The graphic, schematic, and practical contents with which they are created, provide practical information on the 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 be able to download all the content to any electronic device from the Virtual Campus and consult it whenever you need it, even without an Internet connection”   

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.   

You will get a comprehensive learning with the latest methodology applied to academic teachings, the TECH Relearning"

 Boost your professional career with a university qualification in which you will immerse yourself in the transfer of knowledge to artificial neurons"

The current syllabus has been designed considering the pedagogical methodology that characterizes TECH , known as Relearning. Pioneers in its use, this learning technique guarantees that the professional has a more natural and effective academic experience by reiterating the most important concepts in Neural Networks in Deep Learning throughout the program. This not only leads to a better assimilation of the syllabus, but also to a significant reduction in the number of hours of study required to complete the Postgraduate Certificate. 

A syllabus that will provide you with the skills necessary to set up a metric or loss function in trainer and optimizer” 

Module 1. Neural Networks, the Basis of Deep Learning

1.1. Deep Learning

1.1.1. Types of Deep Learning
1.1.2. Applications of Deep Learning
1.1.3. Advantages and Disadvantages of Deep Learning

1.2. Operations

1.2.1. Addition
1.2.2. Product
1.2.3. Transfer

1.3. Layers

1.3.1. Input layer
1.3.2. Hidden layer
1.3.3. Output layer

1.4. Layer Union and Operations

1.4.1. Design of Architectures
1.4.2. Connection between Layers
1.4.3. Forward Propagation

1.5. Construction of the First Neural Network

1.5.1. Network Design
1.5.2. Weights Establishment
1.5.3. Network Training

1.6. Trainer and Optimizer

1.6.1. Optimizer Selection
1.6.2. Establishment of a Loss Function
1.6.3. Establishment of a Metric

1.7. Application of the Neural Network Principles

1.7.1. Activation Functions
1.7.2. Backward Propagation
1.7.3. Parameter Adjustment

1.8. From Biological to Artificial Neurons

1.8.1. Functioning of a Biological Neuron
1.8.2. Knowledge Transfer to Artificial Neurons
1.8.3. Establishing Relations between Both

1.9. Implementation of MLP (Multilayer Perceptron) with Keras

1.9.1. Definition of the Network Structure
1.9.2. Model Compilation
1.9.3. Model Training

1.10. Fine Tuning Hyperparameters of Neural Networks

1.10.1. Selection of the Activation Function
1.10.2. Learning Rate Establishment
1.10.3. Weight Adjustment

A comprehensive program designed by experts for you to acquire in-depth knowledge in Neural Networks in Deep Learning”