Thanks to this 100% online postgraduate certificate, you will master the fundamentals of Deep Learning and design the most efficient architectures for specific tasks such as sentiment analysis"

Deep Learning is so versatile and offers so many applications that it has become one of the most relevant technologies today. In this sense, professionals use Deep Learning tools to better understand customer behavior and adapt their marketing strategies in order to build customer loyalty.  In addition, these models are used to predict consumer preferences based on aspects such as purchase history, website navigation and even ad clicks. In this way, specialists personalize product recommendations and offers for each individual, optimizing their experience while companies increase their conversion rates. 

In this scenario, TECH develops a pioneering program in Mathematical Basis of Deep Learning. Thanks to this program, developers will gain a solid understanding of Deep Learning algorithms and implement them to neural network models. The curriculum will delve into essential concepts such as derivatives of linear functions, Backward Pass and parameter optimization. The syllabus will also focus on the use of Supervised Learning machines. Students will nurture their practice with the most innovative models to be used in procedures with labeled data.  The syllabus will also emphasize the importance of model training, offering advanced techniques including Online Learning. Thanks to this, graduates will ensure that their devices learn from the data in order to perform activities accurately. 

Moreover, the program features the revolutionary Relearning methodology, based on the reiteration of key content and experience, offering simulation cases for a direct approach of professionals with the current challenges in Deep Learning. Students will enjoy a variety of didactic materials in different formats such as interactive videos, complementary readings and practical exercises.

You will master the Batch Learning approach at the world's best digital university according to Forbes"  

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 Mathematical Basis of Deep Learning
• The graphical, schematic and practical contents with which it is conceived gather technological and practical information on those 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 master Decision Tree models to effectively solve you a variety of classification problems in different areas"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities. 

The 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 immersive education programmed to learn in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

Do you want to specialize in hyperparameter adjustment? Achieve it with this program in only 300 hours"

With the Relearning system you will focus on the most relevant concepts without having to invest a large amount of study hours"

Upon completion of this postgraduate certificate, graduates will have a holistic view of the Mathematical Basis of Deep Learning. This will enable professionals to apply the concepts of functions and their derivatives to Deep Learning algorithms for devices to automate complex tasks. Likewise, experts will master the various systems of Supervised Learning, among which Decision Trees or Neural Networks models stand out. In this way, developers will provide solutions in a wide range of applications such as natural language recognition, text generation or automatic translations.

You will implement in your projects the most effective Optimization Methods for training Deep Learning models”  

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

Updating your knowledge of the mathematical foundations of Deep Learning will be much easier thanks to the multimedia material provided by this program"