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"

Students enrolled in this program will have the opportunity to develop advanced knowledge that will enable them to enhance their career prospects in the technology sector, especially in the development of Artificial Intelligence. To help students achieve their goals, this academic institution offers innovative and easily accessible pedagogical tools, as well as top-notch faculty with extensive backgrounds in the field of AI.

Achieves the objectives of the title and develops the Chain Rule for calculating derivatives of nested functions”

General Objectives

• Fundamentalize the key concepts of mathematical functions and their derivatives
• Apply these principles to deep learning algorithms to learn automatically
• Examine the key concepts of Supervised Learning and how they apply to neural network models
• Analyze the training, evaluation and analysis of neural network models
• Fundamentals of the key concepts and main applications of deep learning
• Implement and optimize neural networks with Keras
• Develop expertise in the training of deep neural networks
• Analyze the optimization and regularization mechanisms required for deep neural network training

Specific Objectives

• Develop the chain rule for calculating derivatives of nested functions
• Analyze how to create new functions from existing functions and how to compute the derivatives of these functions
• Examine the concept of Backward Pass and how derivatives of vector functions are applied to automatic learning
• Learn how to use TensorFlow to build custom models
• Understand how to load and process data using TensorFlow tools
• Fundamentalize the key concepts of NLP natural language processing with RNN and attention mechanisms
• Explore the functionality of Hugging Face transformer libraries and other natural language processing tools for application to vision problems
• Learn how to build and train autoencoder models, GANs, and diffusion models
• Understand how autoencoders can be used to efficiently encode data
• Analyze how linear regression works and how it can be applied to neural network models
• Understand the rationale for optimizing hyperparameters to improve the performance of neural network models
• Determine how the performance of neural network models can be evaluated using the training set and the test set

Enroll now and take your career in IT to the next level by exploring the functionality of Transformers libraries”