Get up to speed on the mathematical foundations of Deep Learning to create the most advanced neural networks"

Today, Deep Learning has become one of the most widely used techniques in Artificial Intelligence due to its ability to train deep neural networks and perform complex tasks accurately in a wide variety of fields. In Robotics, for example, Deep Learning is used for autonomous navigation and object recognition. In the case of Natural Language Processing, it is valuable for machine translation and the creation of intelligent Chatbots.

However, in order to effectively use these neural networks, it is necessary to have a solid grasp of the underlying mathematical foundations. This is precisely the focus of the Postgraduate certificate in Mathematical Foundations of Deep Learning, which is designed to provide students with a foundation in Advanced Mathematics and Statistics necessary for deep learning.

The program is structured around topics dealing with Linear Algebra, Multivariable Calculus, Optimization and Probability. In this sense, students will go through key concepts such as matrices, vectors, partial derivatives, Downward Gradient, probability distributions or Inferential Statistics. In addition, the degree also includes several examples and practical exercises to help students apply the theoretical concepts in a real context.

The best part is that this Postgraduate certificate is 100% online, which means that enrollees can access the program materials from anywhere in the world and at any time that is convenient for them.

You will be an expert in operations with vector functions and their derivatives”

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 practical cases presented by experts in Mathematical Foundations of Deep Learning
• The graphic, schematic and eminently practical contents with which it is conceived gather technological 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

Get all the keys to master the operation of the models that operate under Supervised Learning”

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. This will be done with the help of an innovative system of interactive videos made by renowned experts.

Compare data sets with mastery thanks to the innovative teaching resources of the Virtual Campus"

You will specialize in adjusting hyperparameters or handling regularization techniques in only 300 hours"

The curriculum of this Postgraduate certificate will guide students through a comprehensive exploration of the mathematical foundations of Deep Learning in an academic journey condensed into 300 hours. Students will also have access to a wide range of innovative teaching resources available on the program's Virtual Campus, which will complement and enrich their learning experience. Some of them are self-assessment exercises, case studies or interactive summaries.

A curriculum that captures each and every one of the principles of Deep Learning”

Module 1. Mathematical Basis of Deep Learning

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. Addition of functions
1.7.2. Product of functions
1.7.3. Composition of functions

1.8. Derivatives of functions with multiple vector entries

1.8.1. Derivatives of linear functions
1.8.2. Derivatives of nonlinear functions
1.8.3. Derivatives of Compound Functions

1.9. Vector functions and their derivatives: A step further

1.9.1. Directional derivatives
1.9.2. Mixed derivatives
1.9.3. Matrix derivatives

1.10. The Backward Pass

1.10.1. Error propagation
1.10.2. Applying update 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. Prediction 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 graphs

2.10. Hyperparameters

2.10.1. Selection of hyperparameters
2.10.2. Parameter search
2.10.3. Hyperparameter tuning 

Take the opportunity to enroll in the perfect degree to delve into the explainability of artificial neural network models”