Thanks to the solid knowledge provided by this program, you will enter into this important branch of artificial intelligence focused on the construction of algorithms inspired by the functioning of the human brain with an effective methodology and in a 100% online format” 

Deep Learning is one of the main technologies behind artificial intelligence and has driven many advances in fields such as computer vision, natural language processing and robotics. For example, Amazon Alexa's voice recognition technology has a 95% accuracy rate based on this branch of AI. Therefore, it can be used to solve important problems in society, such as the early detection of diseases, the forecasting of natural disasters and the fight against climate change. For example, Deep Learning has been used to forecast the melting of glaciers with an accuracy of 96%. 

In this context, TECH has designed a comprehensive program in which students will delve into the principles of Deep Learning and delve into its mathematical foundations. Therefore, taking this program is an excellent option for professional growth due to the growing demand for professionals qualified in the area, the increase in investment in AI, its diverse applications, the resources and support communities available, the intellectual challenge it presents, and its potential for innovation. 

And to facilitate student learning, TECH has created this complete program based on the exclusive Relearning methodology. A teaching process designed for the graduate to integrate the fundamental concepts in a progressive and natural way through repetition. This way, they will acquire the necessary competencies by adjusting the study to their life style.   

All this is presented in a totally online format. In this way, the professional focuses only on their learning, without the need to travel or adjust to a pre-established timing. In addition, you can access the theoretical and practical content from anywhere and at any time, you only need a device with an Internet connection.   

Specialize in a booming sector with great projection and you will be able to excel in a wide variety of applications, such as computer vision, natural language processing, robotics and voice recognition”   

This Postgraduate diploma in Deep Learning 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 
• 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 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 

With the Relearning methodology you will acquire knowledge progressively and with total flexibility. A program that adapts to you”

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.   

Combine your personal and work responsibilities with your studies thanks to this Postgraduate diploma. 100% flexible and online"

Learn how to perform evaluation metrics and determine Deep Learning optimization methods thanks to this exclusive program"

The Relearning method, which is based on the constant repetition of key concepts throughout the course, will allow the engineer to obtain advanced and effective learning without having to dedicate long hours of study. In this way, the professional will be able to delve into a complete syllabus on nested functions, neural network models or deep learning applications.  

You will have access to a syllabus designed by experts and with high quality content for you to achieve successful learning”  

Module 1. Deep Learning Mathematical Fundamentals

1.1. Functions and Derivatives

1.1.1. Linear Functions
1.1.2. Partial Derivative
1.1.3. Higher Order Derivatives

1.2. Multiple Nested Functions

1.2.1. Compound Functions
1.2.2. Inverse Functions
1.2.3. Recursive Functions

1.3. Chain Rule

1.3.1. Derivatives of Nested Functions
1.3.2. Derivatives of Compound Functions
1.3.3. Derivatives of Inverse Functions

1.4. Functions with Multiple Inputs

1.4.1. Multi-variable Functions
1.4.2. Vectorial Functions
1.4.3. Matrix Functions

1.5. Derivatives of Functions with Multiple Inputs

1.5.1. Partial Derivative
1.5.2. Directional Derivatives
1.5.3. Mixed Derivatives

1.6. Functions with Multiple Vector Inputs

1.6.1. Linear Vector Functions
1.6.2. Non-linear Vector Functions
1.6.3. Matrix Vector Functions

1.7. Creating New Functions from Existing Functions

1.7.1. Function Addition
1.7.2. Function Product
1.7.3. Function Composition

1.8. Derivatives of Functions with Multiple Vector Inputs

1.8.1. Derivatives of Linear Functions
1.8.2. Derivatives of Non-linear Functions
1.8.3. Derivatives of Compound Functions

1.9. Vector Functions and their Derivatives: One Step Further

1.9.1. Directional Derivatives
1.9.2. Mixed Derivatives
1.9.3. Matrix Derivatives

1.10. Backward Pass

1.10.1. Error Propagation
1.10.2. Application of Updating Rules
1.10.3. Parameter Optimization

Module 2. Deep Learning Principles

2.1. Supervised Learning

2.1.1. Supervised Learning Machines
2.1.2. Uses of Supervised Learning
2.1.3. Differences Between Supervised and Unsupervised Learning

2.2. Supervised Learning Models

2.2.1. Linear Models
2.2.2. Decision Tree Models
2.2.3. Neural Network Models

2.3. Linear Regression

2.3.1. Simple Linear Regression
2.3.2. Multiple Linear Regression
2.3.3. Regression Analysis

2.4. Model Training

2.4.1. Batch Learning
2.4.2. Online Learning
2.4.3. Optimization Methods

2.5. Model Evaluation: Training Set vs. Test Set

2.5.1. Evaluation Metrics
2.5.2. Cross Validation
2.5.3. Comparison of Data Sets

2.6. Model Evaluation: The Code

2.6.1. Forecast Generation
2.6.2. Error Analysis
2.6.3. Evaluation Metrics

2.7. Variables Analysis

2.7.1. Identification of Relevant Variables
2.7.2. Correlation Analysis
2.7.3. Regression Analysis

2.8. Explainability of Neural Network Models

2.8.1. Interpretable Models
2.8.2. Visualization Methods
2.8.3. Evaluation Methods

2.9. Optimization

2.9.1. Optimization Methods
2.9.2. Regularization Techniques
2.9.3. The Use of Graphics

2.10. Hyperparameters

2.10.1. Hyperparameters Selection
2.10.2. Parameter Search
2.10.3. Hyperparameters Adjustment

Module 3. Neural Networks, the Basis of Deep Learning

3.1. Deep Learning

3.1.1. Types of Deep Learning
3.1.2. Applications of Deep Learning
3.1.3. Advantages and Disadvantages of Deep Learning

3.2. Operations

3.2.1. Addition
3.2.2. Product
3.2.3. Transfer

3.3. Layers

3.3.1. Input layer
3.3.2. Hidden layer
3.3.3. Output layer

3.4. Layer Union and Operations

3.4.1. Design of Architectures
3.4.2. Connection between Layers
3.4.3. Forward Propagation

3.5. Construction of the First Neural Network

3.5.1. Network Design
3.5.2. Weights Establishment
3.5.3. Network Training

3.6. Trainer and Optimizer

3.6.1. Optimizer Selection
3.6.2. Establishment of a Loss Function
3.6.3. Establishment of a Metric

3.7. Application of the Neural Network Principles

3.7.1. Activation Functions
3.7.2. Backward Propagation
3.7.3. Parameter Adjustment

3.8. From Biological to Artificial Neurons

3.8.1. Functioning of a Biological Neuron
3.8.2. Knowledge Transfer to Artificial Neurons
3.8.3. Establishing Relations between Both

3.9. Implementation of MLP (Multilayer Perceptron) with Keras

3.9.1. Definition of the Network Structure
3.9.2. Model Compilation
3.9.3. Model Training

3.10. Fine Tuning Hyperparameters of Neural Networks

3.10.1. Selection of the Activation Function
3.10.2. Learning Rate Establishment
3.10.3. Weight Adjustment

A program designed to make you an expert in Deep Learning”