Enroll and get a cutting-edge and effective teaching with TECH Relearning. Forget about memorization and get into efficient learning”

In a constantly evolving environment such as engineering, Deep Learning has become an essential tool for data processing and complex problem solving. Therefore, the Mathematical Basis of Deep Learning is used in fields as diverse as medicine, the automotive industry, fraud detection and financial analysis, among others. That is why the demand for highly qualified professionals in this area is only increasing.

In this context, this TECH program was created to respond to the needs of the market and provide students with a quality education in this discipline. This program has been specifically designed to provide students with a thorough understanding of the fundamental mathematics underlying Deep Learning, including calculus, probability theory and statistics. In addition, students will have the opportunity to acquire advanced skills in programming in Tensorflow and Deep Visual Computer, among other tools. All of this is presented in a 100% online format, which allows students to adapt their studies to their life style and access the theoretical and practical contents from anywhere and at any time.

In order to facilitate the student's learning, TECH has developed a complete program based on the Relearning methodology for the progressive and natural repetition of the fundamental concepts. In this way, the graduate will acquire the necessary competencies at their own pace and adjusted to their life style. In addition, the completely online format will allow the professional to access the theoretical and practical contents from anywhere and at any time through a device with Internet connection, focusing only on learning. In addition, they can access the theoretical and practical content from anywhere and at any time, all you need is a device with Internet connection.

With TECH you will be able to project your professional career without neglecting other areas of your life, that's why it offers you a flexible teaching adapted to your needs”

This Postgraduate certificate in Mathematical Basis of 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 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 

Motivational videos, case studies, graphical and schematic content, discussion forums... Everything you need to give a boost to your professional career. Don't wait any longer"

The program's teaching staff includes professionals from the sector who bring to this training the experience of their work, as well as renowned specialists from reference 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 achieve your goals with the support of a teaching team specialized in neural network models and optimization"

A 100% online program with which you will obtain the most extensive and comprehensive knowledge about functions with multiple inputs and derivatives of functions with multiple inputs"

This syllabus has been created taking into account the pedagogical methodology that distinguishes TECH , the Relearning. Pioneers in its use, this learning technique guarantees that the specialist will obtain a more natural and effective academic experience, reiterating the most important concepts in Mathematical Basis of Deep Learning throughout the program. This way, not only a more effective assimilation of the syllabus is achieved, but also a considerable saving in the hours of study necessary to complete it.

Choose your schedule, pace of study and location. TECHprovides the resources and gives you access to them 24 hours a day”

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

A Postgraduate certificate prepared by experts for you to acquire deep knowledge in the Mathematical Basis of Deep Learning”